Este projeto foi proposto na disciplina FIT 678 - Análise de dados genéticos no melhoramento de plantas da UFV, ministrada pelo Prof. Guilherme da Silva Pereira.

O propósito desse projeto foi realizar análises de ligação e de QTL em um conjunto de dados reais. O objetivo é realizar uma Análise de ligação I: segregação de marcadores, cálculo da fração de recombinação dois-pontos, e formação dos grupos de ligação; Análise de ligação II: ordenação dos marcadores; Análise de QTL I: análise de marcas individuais; Análise de QTL II: mapeamento por intervalo; Análise de QTL III: mapeamento por intervalo composto e Análise de QTL IV: mapeamento por múltiplos intervalos.

DADOS DO MILHO

Trata de uma população RIL com 1106 marcadores em 185 indivíduos e foram avaliados 29 fenótipos que consistem na característica da altura da planta.

library(qtl)
population_Z006 <- read.cross(format="csv", file="population_z006.csv", genotypes=c("0","1","2"), crosstype = "riself")
##  --Read the following data:
##   185  individuals
##   1106  markers
##   29  phenotypes
##  --Cross type: riself
summary(population_Z006)
##     RI strains via selfing
## 
##     No. individuals:    185 
## 
##     No. phenotypes:     29 
##     Percent phenotyped: 100 100 100 100 100 100 100 100 100 100 100 100 100 100 
##                         100 100 100 100 100 100 100 100 100 100 100 100 100 100 
##                         100 
## 
##     No. chromosomes:    1 
##         Autosomes:      0 
## 
##     Total markers:      1106 
##     No. markers:        1106 
##     Percent genotyped:  92.1 
##     Genotypes (%):      AA:48.4  BB:51.6
geno.image(population_Z006)

gt.population_z006 <- geno.table(population_Z006)
gt.population_z006
##                chr missing  AA  BB      P.value
## PZA02255.2       0      10  87  88 9.397430e-01
## PZA00240.6       0      22  85  78 5.834981e-01
## PZA01879.1       0      16  80  89 4.887441e-01
## PZB02155.1       0       9  83  93 4.509823e-01
## PZA02527.2       0       2  99  84 2.675027e-01
## PZA03742.1       0      12  91  82 4.938127e-01
## PZA00521.3       0      12 107  66 1.825948e-03
## PZB02002.1       0       5  86  94 5.509850e-01
## PZA02247.1       0      19  82  84 8.766399e-01
## PZA03529.1       0       9  84  92 5.464936e-01
## ae1.8.7          0      15  70 100 2.139757e-02
## PZA03212.3       0      12  93  80 3.229706e-01
## PZA02941.7       0      18  82  85 8.164239e-01
## PZA02191.1       0      25  88  72 2.059032e-01
## PZA03699.1       0      13  65 107 1.362545e-03
## PZA03728.1       0      11  82  92 4.483923e-01
## PHM4341.42       0       9  85  91 6.510766e-01
## PZA01537.2       0      12  87  86 9.393964e-01
## PZA03227.1       0       2  87  96 5.058592e-01
## PZA02272.3       0      13  68 104 6.051564e-03
## PZA02402.1       0      11  80  94 2.885367e-01
## PZA02454.2       0      11  85  89 7.617076e-01
## PZA01035.1       0      15  82  88 6.453877e-01
## PZA02208.1       0      12  74  99 5.733938e-02
## PZA01044.1       0       9  91  85 6.510766e-01
## PHM532.23        0      16  71  98 3.780866e-02
## PZA02418.2       0       3  82 100 1.821223e-01
## PZA01369.1       0       4  89  92 8.235447e-01
## PZA00225.8       0      13  82  90 5.418656e-01
## PZA03634.1       0      14  80  91 4.002409e-01
## lac1.3           0      13  81  91 4.457659e-01
## PZA01875.1       0       6  85  94 5.011435e-01
## PZA02513.1       0      12  62 111 1.950050e-04
## PZA00616.13      0       8  80  97 2.013206e-01
## PZA01954.1       0      33  88  64 5.157586e-02
## PZA03305.7.1     0      17  88  80 5.370940e-01
## PZA01658.1       0      12  89  84 7.038393e-01
## PHM4786.9        0      51  64  70 6.042343e-01
## PZA00760.1       0       7  86  92 6.529131e-01
## PZA01332.2       0      11  99  75 6.884504e-02
## PZA03469.1       0      11  83  91 5.441971e-01
## PZA01623.3       0       6  84  95 4.109753e-01
## PZA02654.3       0       7  76 102 5.132142e-02
## PZA00355.2       0       8  86  91 7.070485e-01
## PZA02792.26.25   0      13  82  90 5.418656e-01
## PZB00761.1       0      11  81  93 3.629714e-01
## PHM7922.8        0      12  85  88 8.195796e-01
## PZA00706.16      0      22  81  82 9.375687e-01
## PZA00589.10      0      11  83  91 5.441971e-01
## PZA01278.2       0      13  90  82 5.418656e-01
## PZA02087.2       0       9  91  85 6.510766e-01
## sh1.12.11        0       9 101  75 5.001640e-02
## PZA02252.2       0       6  86  93 6.008319e-01
## PZA02457.1       0       7  67 111 9.739714e-04
## PZA03166.1       0      10  82  93 4.056789e-01
## PZA01726.1       0       6  82  97 2.622229e-01
## PZA02359.10      0      14  92  79 3.201572e-01
## PZA02117.1       0       5  78 102 7.363827e-02
## zb27.1           0      14  81  90 4.912971e-01
## PZA02113.1       0      10  84  91 5.967012e-01
## PZA02688.2       0      12  85  88 8.195796e-01
## PZB00079.4       0      13  91  81 4.457659e-01
## PZA02128.3       0       7  91  87 7.643200e-01
## PZA01759.1       0      10  62 113 1.156173e-04
## PZA01154.1       0      26  76  83 5.788016e-01
## PZA02291.1       0      13  83  89 6.473148e-01
## PZA00193.2       0       9 103  73 2.373852e-02
## PHM904.21        0      13  83  89 6.473148e-01
## PZA03488.1       0      16  83  86 8.174941e-01
## PZA00680.3       0       4  75 106 2.121075e-02
## PHM14055.6       0      10  62 113 1.156173e-04
## PZA01316.1       0       9  82  94 3.657123e-01
## PZA03116.1       0      32  70  83 2.932642e-01
## PZA03037.2       0      14  87  84 8.185458e-01
## PZA00838.2       0      27  78  80 8.735811e-01
## PHM13020.10      0      14  83  88 7.021947e-01
## PHM1968.22       0      11  97  77 1.294698e-01
## PZA02325.4       0       7  84  94 4.535368e-01
## PHM3342.31       0      26  76  83 5.788016e-01
## PZA02011.1       0      10  88  87 9.397430e-01
## PHM5526.25       0       9  94  82 3.657123e-01
## PZA01050.1       0      22  71  92 1.000014e-01
## PZB01235.4       0      12  99  74 5.733938e-02
## PZA00498.5       0      20  86  79 5.857884e-01
## PZA03457.1       0       5  96  84 3.710934e-01
## PZA01367.2       0      12 103  70 1.210928e-02
## PZA01796.1       0      16  72  97 5.447039e-02
## PZB01403.1       0      13  86  86 1.000000e+00
## PZA01348.1       0      26  77  82 6.917172e-01
## PHM10404.8       0      22  88  75 3.085646e-01
## PZA03024.16      0      13  69 103 9.528791e-03
## PZA00323.3       0       8  85  92 5.987824e-01
## PZA01866.1       0      21  79  85 6.394119e-01
## PZA01618.2       0      16  80  89 4.887441e-01
## PZA01995.2       0      12  82  91 4.938127e-01
## PHM3078.12       0      24  75  86 3.859851e-01
## PZA00948.1       0       4  76 105 3.111858e-02
## PZB00765.1       0      13  62 110 2.522490e-04
## PZA00675.1       0       6  88  91 8.225779e-01
## PZA02236.1       0      12  87  86 9.393964e-01
## PZA01563.1       0      33  68  84 1.943659e-01
## PHM6111.5        0      20  77  88 3.918049e-01
## PZA02961.6       0      11  83  91 5.441971e-01
## PZA02393.2       0      14  86  85 9.390437e-01
## PZA00084.2       0      12  86  87 9.393964e-01
## PZB01021.1       0      11 100  74 4.871760e-02
## PZA02577.1       0      17  90  78 3.545395e-01
## PZA02367.1       0       4 105  76 3.111858e-02
## PZA02386.2       0      43  72  70 8.667121e-01
## PHM1870.20       0      18  74  93 1.414902e-01
## PZA02129.1       0       6  96  83 3.312169e-01
## PZA02480.1       0       1  74 110 7.955439e-03
## PZA01280.2       0      11  88  86 8.794870e-01
## PZA02678.1       0      15  91  79 3.573857e-01
## PZA03747.1       0      13  65 107 1.362545e-03
## PZB01662.1       0      10  88  87 9.397430e-01
## PZA02955.3       0       9  77  99 9.725443e-02
## PZA00068.1       0      10  98  77 1.124106e-01
## PZA00436.7       0      10  73 102 2.836551e-02
## PHM4503.25       0      15  86  84 8.780884e-01
## PHM1307.11       0      12  60 113 5.589196e-05
## PZA03275.4.1     0       8  92  85 5.987824e-01
## PHM18705.23      0      13  85  87 8.787937e-01
## PZA00508.2       0      32  80  73 5.714506e-01
## PHM7616.35       0       4  95  86 5.035180e-01
## glb1.2           0      29  79  77 8.727801e-01
## PZA01993.7       0       9  92  84 5.464936e-01
## PZA00455.14.16   0      12  94  79 2.541077e-01
## PZB00942.1       0      15  83  87 7.590063e-01
## PZA02289.2       0      11  95  79 2.251463e-01
## PZA03577.1       0       5  95  85 4.560565e-01
## PHM816.29        0       6  87  92 7.086145e-01
## PZA00163.4       0       4  88  93 7.101556e-01
## PHM4353.31       0      13  83  89 6.473148e-01
## PZA01691.1       0      11  73 101 3.378114e-02
## PHM6428.11       0      21  82  82 1.000000e+00
## PZA02779.1       0      11  99  75 6.884504e-02
## PHM5484.22       0       9  79  97 1.748444e-01
## PZA01638.1       0      21  83  81 8.758961e-01
## PZA01241.2       0      10  86  89 8.205958e-01
## PHM1959.26       0      13  80  92 3.601961e-01
## PZA02948.24      0       9  89  87 8.801685e-01
## PHM3330.25       0      10  86  89 8.205958e-01
## PZA01238.1.2     0      11  94  80 2.885367e-01
## PZA03070.9       0      13  86  86 1.000000e+00
## PZA02390.1       0      51  35  99 3.225060e-08
## PZA01055.1       0      13  81  91 4.457659e-01
## PZA02081.1       0      21  67  97 1.914957e-02
## PZA00175.2       0      23  77  85 5.296507e-01
## PZA00158.2       0       8  88  89 9.400837e-01
## PZA03717.1       0      21  69  95 4.233023e-02
## PHM9418.11       0      24  89  72 1.803144e-01
## PZA01744.1       0      48  72  65 5.498063e-01
## PZA01141.1       0       9  84  92 5.464936e-01
## PZA00243.25      0       8  94  83 4.083444e-01
## PHM1184.26       0       2  87  96 5.058592e-01
## PHM2770.19       0      18  82  85 8.164239e-01
## PHM10225.15      0      11  90  84 6.492108e-01
## PZA00793.2       0      14  91  80 4.002409e-01
## PZA02068.1       0      66  22  97 6.188613e-12
## PZA03677.1       0      22  71  92 1.000014e-01
## PZA02164.16      0      22  71  92 1.000014e-01
## PZA01936.4       0      11  86  88 8.794870e-01
## zb7.2            0      26  84  75 4.753840e-01
## PZA00425.11      0      33  77  75 8.711315e-01
## PZA03629.1       0      13  94  78 2.224692e-01
## PZA00708.3       0       6  88  91 8.225779e-01
## PHM14104.23      0      25  78  82 7.518296e-01
## PZA01978.23      0       9  92  84 5.464936e-01
## PZA00079.1       0      12  83  90 5.945874e-01
## PZA01901.1       0      10  88  87 9.397430e-01
## PHM1218.6        0      19  95  71 6.249586e-02
## PZA03255.1       0      19  80  86 6.414372e-01
## PZA00106.10      0       6  88  91 8.225779e-01
## PZA00704.1       0      12  73 100 4.009470e-02
## PZA03605.1       0      11  83  91 5.441971e-01
## PZA01962.12      0       7  76 102 5.132142e-02
## PZA00682.17.2    0      10  90  85 7.054570e-01
## PZA03182.5       0      17  84  84 1.000000e+00
## PHM3852.23       0      15  83  87 7.590063e-01
## PZA00804.1       0       4  82  99 2.063736e-01
## PZA02514.1       0      10  85  90 7.054570e-01
## PZA03521.1       0       6  88  91 8.225779e-01
## PZA01038.1       0       9  84  92 5.464936e-01
## PZA02668.2       0      11  86  88 8.794870e-01
## PHM13673.53      0      18  80  87 5.880415e-01
## bt2.7.4          0      10  60 115 3.215956e-05
## PZA02890.4       0      28  75  82 5.763932e-01
## PZA01933.3       0      19  83  83 1.000000e+00
## PZA02002.1       0      11  66 108 1.452491e-03
## PZA01195.3       0       9 101  75 5.001640e-02
## PZA00214.1       0      50  67  68 9.314137e-01
## PZA01597.1       0      14  84  87 8.185458e-01
## PZA02274.1       0       5  93  87 6.547208e-01
## PZA01073.1       0      10  90  85 7.054570e-01
## PZA02235.14      0      25  75  85 4.291953e-01
## PZA00222.7       0      14  76  95 1.462331e-01
## PZA01445.1       0      12  84  89 7.038393e-01
## PZA01360.3       0      14  84  87 8.185458e-01
## PZA03155.3       0      15 100  70 2.139757e-02
## PZA00118.1.5     0       9  85  91 6.510766e-01
## PZA02168.1       0      12  95  78 1.961889e-01
## PZA00511.3       0      22  80  83 8.142257e-01
## PZA00058.1       0      23  77  85 5.296507e-01
## PZA01693.1       0      12  77  96 1.485862e-01
## PZA01294.2.1     0      14  72  99 3.894746e-02
## PZA03391.1       0      12  86  87 9.393964e-01
## PZA00307.14      0      30  83  72 3.769439e-01
## PZA00525.17      0      10  71 104 1.261114e-02
## PZA00223.4       0      17  87  81 6.434288e-01
## PZA01909.1.2     0       7  82  96 2.940197e-01
## PZA00453.2       0       6  85  94 5.011435e-01
## PZA01289.1       0       8  88  89 9.400837e-01
## PZA02479.1       0      11 100  74 4.871760e-02
## PHM6238.36       0       7  88  90 8.808385e-01
## PZA02578.1       0      10  89  86 8.205958e-01
## PZA00416.7       0       7  87  91 7.643200e-01
## PHM824.17        0      16  81  88 5.902585e-01
## PZA01527.1       0      10  86  89 8.205958e-01
## PZB00232.2       0      16  76  93 1.909777e-01
## PZA01386.3       0      20  94  71 7.336593e-02
## PZA02470.2       0      23  78  84 6.373519e-01
## PZA03198.3       0       6  88  91 8.225779e-01
## PZA00694.6       0      11 106  68 3.967018e-03
## PZA03172.3       0      17  70  98 3.075356e-02
## PZA03320.6       0      18  69  98 2.482678e-02
## PZA01753.1       0      13  72 100 3.276265e-02
## PZB01457.1       0      34  70  81 3.706977e-01
## PHM1275.22       0      16  92  77 2.485632e-01
## PZA02741.1       0      34  84  67 1.665299e-01
## PHM14152.18      0       7  87  91 7.643200e-01
## PHM11985.27      0      12  84  89 7.038393e-01
## zb21.1           0      11  86  88 8.794870e-01
## kip1.3           0      18  81  86 6.988216e-01
## PZA00265.6       0      12  89  84 7.038393e-01
## PZA03317.1       0      17  70  98 3.075356e-02
## PZA03650.1       0      22  81  82 9.375687e-01
## PZA00409.17      0      13  84  88 7.603683e-01
## PHM3626.3        0      25  79  81 8.743671e-01
## PZA03165.1       0      21  78  86 5.321712e-01
## PZA02260.2       0       4  95  86 5.035180e-01
## PZA00402.1       0      12  85  88 8.195796e-01
## PZA03193.2       0       9  85  91 6.510766e-01
## PHM11114.7       0      10  92  83 4.962917e-01
## PZA00494.2       0      24  76  85 4.781387e-01
## PZA02151.3       0      12 106  67 3.025697e-03
## PZA01533.2       0      16  87  82 7.005224e-01
## PZA02663.1       0       9  83  93 4.509823e-01
## PHM2100.21       0      10  98  77 1.124106e-01
## an1.5            0      14  96  75 1.082937e-01
## PZA03142.5       0      12  85  88 8.195796e-01
## PZA01365.1       0      11  76  98 9.535232e-02
## PZA01790.1       0      10  99  76 8.209871e-02
## PZA02699.1       0      14  88  83 7.021947e-01
## PZA00100.10      0      15  92  78 2.829343e-01
## PZA01267.3       0      16  96  73 7.685537e-02
## PZA02645.2       0      12  88  85 8.195796e-01
## PZA00282.19      0       8  92  85 5.987824e-01
## PHM13360.13      0      13  87  85 8.787937e-01
## PZA01902.1       0      13  87  85 8.787937e-01
## PZA00881.1       0      13  76  96 1.272627e-01
## PHM16437.20      0      13  85  87 8.787937e-01
## PZA00186.4       0      15  77  93 2.197685e-01
## PZA02408.2       0      17  70  98 3.075356e-02
## PZA00362.1       0      13  75  97 9.344782e-02
## PZA01509.1       0      10  88  87 9.397430e-01
## PHM13623.14      0      11  62 112 1.503503e-04
## PZA02698.3       0      10  97  78 1.509270e-01
## PZA02316.22      0      18  90  77 3.144299e-01
## PZA02769.1       0       1  74 110 7.955439e-03
## PZA00509.1       0      13  88  84 7.603683e-01
## PZA01963.15      0      32  76  77 9.355651e-01
## PZB01461.1       0      12 103  70 1.210928e-02
## PZA02853.11      0      13  84  88 7.603683e-01
## PZA03069.8.4     0       8  87  90 8.215951e-01
## PZA03573.1       0       1  89  95 6.582534e-01
## PHM15474.5       0      12  88  85 8.195796e-01
## PZA00939.1       0      24  89  72 1.803144e-01
## PZA02862.3       0      13  75  97 9.344782e-02
## PZA02815.25      0       4  88  93 7.101556e-01
## PZA01672.1       0      49  66  70 7.316006e-01
## PHM2828.83       0      64  59  62 7.850629e-01
## PZA01187.1       0      11  92  82 4.483923e-01
## PZA02417.2       0      43  61  81 9.327631e-02
## PZA00752.1       0      16  94  75 1.438677e-01
## PZA03188.3       0       8  90  87 8.215951e-01
## PZA00878.2       0      13 103  69 9.528791e-03
## PHM11226.13      0       7  86  92 6.529131e-01
## PZA00996.1       0      33  68  84 1.943659e-01
## PZA03735.1       0      35  67  83 1.914184e-01
## PHM6043.19       0      12  82  91 4.938127e-01
## PZA00081.18      0      12  94  79 2.541077e-01
## PZA00395.2       0      17  59 109 1.145134e-04
## PZA01391.1       0      32  76  77 9.355651e-01
## PZA00343.31      0      12  95  78 1.961889e-01
## PZB00901.3.4     0      13  68 104 6.051564e-03
## PZA02522.1       0       9  91  85 6.510766e-01
## PHM14412.4       0      21  78  86 5.321712e-01
## PZA00181.2       0       6  88  91 8.225779e-01
## PZA00978.1       0       8  93  84 4.987350e-01
## PZA02299.16      0       5  87  93 6.547208e-01
## PZB00094.1       0      26  83  76 5.788016e-01
## PHM2343.25       0      11  87  87 1.000000e+00
## PHM4913.18       0      24  85  76 4.781387e-01
## PHM3726.129      0      13  89  83 6.473148e-01
## PZA03638.1       0      13  84  88 7.603683e-01
## PZA01779.1       0      13  75  97 9.344782e-02
## PZA02337.4       0      11  75  99 6.884504e-02
## PZA00155.1       0      10 101  74 4.125002e-02
## PZA01552.1       0      16  80  89 4.887441e-01
## PZA01619.1       0      12  85  88 8.195796e-01
## PZA01791.2       0      11  81  93 3.629714e-01
## PZA00424.1       0      48  72  65 5.498063e-01
## PZA03741.1       0      12  80  93 3.229706e-01
## PHM4468.13       0      10  84  91 5.967012e-01
## PZA02175.1       0      10  72 103 1.910992e-02
## PZA03559.1       0      43  61  81 9.327631e-02
## PHM3094.23       0      20  84  81 8.153346e-01
## PZA01232.1       0      13  87  85 8.787937e-01
## PZA00213.19      0       8  85  92 5.987824e-01
## PZA03527.1       0      14  91  80 4.002409e-01
## PZB02122.1       0       8  77 100 8.384743e-02
## PZA02465.1       0      11  90  84 6.492108e-01
## PZA00210.1.9     0       9  88  88 1.000000e+00
## PZA02048.2       0      15  80  90 4.431023e-01
## PZA00887.1       0      17  82  86 7.576207e-01
## PZA01601.1       0       5  89  91 8.814975e-01
## PZB00544.2       0       8  91  86 7.070485e-01
## PHM14046.9       0       8  89  88 9.400837e-01
## PZA01114.2       0      11  86  88 8.794870e-01
## PZA00497.4       0      14  77  94 1.935933e-01
## PHM4125.11       0      10 100  75 5.878172e-02
## PZA01425.2       0       9  85  91 6.510766e-01
## PZA02203.1       0       9  95  81 2.912928e-01
## PZA01135.1       0      14  94  77 1.935933e-01
## PZA03385.1       0      12  61 112 1.055535e-04
## PZA00273.5       0      13  76  96 1.272627e-01
## PZA01530.1       0      33  68  84 1.943659e-01
## PZA01729.1       0      14  83  88 7.021947e-01
## PHM5359.10       0      18  90  77 3.144299e-01
## PZA02017.1       0       9  84  92 5.464936e-01
## PZA01877.2       0      13  86  86 1.000000e+00
## PHM4926.16       0      32  74  79 6.860465e-01
## PZA01919.2       0       9  91  85 6.510766e-01
## PZA02135.2       0      34  84  67 1.665299e-01
## PZA00739.1       0      12  85  88 8.195796e-01
## PZA00647.9       0      12  85  88 8.195796e-01
## PZA01303.1       0      16  73  96 7.685537e-02
## PZA00463.3       0      12  84  89 7.038393e-01
## PZB00752.1       0       9  80  96 2.278000e-01
## PZA02262.3       0      13  81  91 4.457659e-01
## PZA01607.1       0      12  84  89 7.038393e-01
## PZA02564.2       0       4  87  94 6.028504e-01
## PZA02194.1       0      11  95  79 2.251463e-01
## PZA03603.1       0      11  83  91 5.441971e-01
## PZA00541.1       0      10  62 113 1.156173e-04
## PHM3457.6        0      24  82  79 8.130966e-01
## PZA01921.20.19   0       4  96  85 4.135722e-01
## PZA01089.1       0      10  85  90 7.054570e-01
## PZA02141.1       0       9  87  89 8.801685e-01
## PZA02746.2       0      22  75  88 3.085646e-01
## PZA01713.4       0      11  62 112 1.503503e-04
## PHM13823.7       0      11  86  88 8.794870e-01
## PZD00022.5       0       3  95  87 5.531815e-01
## PZA00390.7       0       7  86  92 6.529131e-01
## PZA00513.1       0      11 106  68 3.967018e-03
## PZA02182.1       0      14  83  88 7.021947e-01
## PZA01060.1       0       1  74 110 7.955439e-03
## PZB00183.4       0      12  95  78 1.961889e-01
## PZA00986.1       0       8  82  95 3.284999e-01
## PZA01239.2       0       4  95  86 5.035180e-01
## PZA00693.3       0       9  86  90 7.630246e-01
## PZA03189.4       0      16  95  74 1.062274e-01
## PZA03152.3       0       8  73 104 1.980072e-02
## PZA00332.5       0      12 101  72 2.746608e-02
## PZB01944.1       0      11  95  79 2.251463e-01
## PZA03270.2       0      11  60 114 4.244896e-05
## PHM15501.9       0      13  83  89 6.473148e-01
## PZA00667.2       0      12  74  99 5.733938e-02
## PZA01140.1       0      13  41 131 6.769623e-12
## PZA01211.1       0      11  70 104 9.950828e-03
## PZA00571.1       0      13  81  91 4.457659e-01
## PZA02549.3       0      13  87  85 8.787937e-01
## PZA00495.5       0      21  83  81 8.758961e-01
## PHM10321.11      0      24  82  79 8.130966e-01
## PHM3993.28       0      13  83  89 6.473148e-01
## PZA01292.1       0       8  92  85 5.987824e-01
## PZA01501.1       0      12  85  88 8.195796e-01
## PZA03607.1       0      32  76  77 9.355651e-01
## PHM5794.13       0      17  87  81 6.434288e-01
## PZA01327.1       0       9  86  90 7.630246e-01
## PZA03645.1       0      13  83  89 6.473148e-01
## PZA00643.13      0      13  75  97 9.344782e-02
## PZA03228.4       0      12  79  94 2.541077e-01
## PZA00912.2       0       8  82  95 3.284999e-01
## PZA01766.1       0      33  88  64 5.157586e-02
## PZA02490.1       0      29  76  80 7.487740e-01
## PHM3226.15       0      24  79  82 8.130966e-01
## PZA01005.1       0      11  85  89 7.617076e-01
## PZA02049.1       0      14  87  84 8.185458e-01
## PZA02612.1       0      11  86  88 8.794870e-01
## PHM12990.15      0      11  86  88 8.794870e-01
## PZA00060.2       0       9  86  90 7.630246e-01
## PZA03687.1       0      12  84  89 7.038393e-01
## PZA00543.12      0       8  85  92 5.987824e-01
## PZA00750.1       0      10  82  93 4.056789e-01
## PZA01887.1       0       3 107  75 1.769220e-02
## PZA03561.1       0      16  93  76 1.909777e-01
## PZA02453.1       0      16  81  88 5.902585e-01
## PZA02344.1       0       6 102  77 6.168019e-02
## PZA00951.1       0      12  88  85 8.195796e-01
## PZA02371.6       0      12  87  86 9.393964e-01
## PZA00749.1       0      12  92  81 4.029780e-01
## PZA03587.1       0      10  61 114 6.164510e-05
## PZA01315.1       0      13  94  78 2.224692e-01
## PHM4134.8        0      18  82  85 8.164239e-01
## PZA03211.6       0      14  86  85 9.390437e-01
## PZA03184.2       0       9  81  95 2.912928e-01
## fea2.3           0      12  73 100 4.009470e-02
## PZA00447.8       0      23  77  85 5.296507e-01
## PZA02474.1       0       5  80 100 1.360371e-01
## PZA02471.5       0       3  82 100 1.821223e-01
## PZA00386.4       0      12  90  83 5.945874e-01
## PZA03597.1       0      12  60 113 5.589196e-05
## PZA02204.1       0       9  91  85 6.510766e-01
## PZA02122.9       0      12  84  89 7.038393e-01
## PZA01304.1       0      17  70  98 3.075356e-02
## PZA02737.1       0      14  93  78 2.513491e-01
## PZB01222.1       0       7  84  94 4.535368e-01
## PZA01049.1       0       9  84  92 5.464936e-01
## PZA02487.1       0      24  79  82 8.130966e-01
## PHM16854.3       0      33  68  84 1.943659e-01
## PZA03073.28.26   0       8  84  93 4.987350e-01
## PZA00020.5       0       9  82  94 3.657123e-01
## PZA00827.1       0       7  77 101 7.203829e-02
## PHM1911.173      0       4  95  86 5.035180e-01
## PZA00758.1       0      14  88  83 7.021947e-01
## PZB00869.4       0      33  68  84 1.943659e-01
## PZD00027.2       0       7  83  95 3.684194e-01
## PZA03531.1       0      10  97  78 1.509270e-01
## PZA03191.1.4     0      35  67  83 1.914184e-01
## PHM13122.43      0       7  91  87 7.643200e-01
## PHM1962.33       0      24  78  83 6.935403e-01
## PZA01072.1       0      10  88  87 9.397430e-01
## PHM2244.142      0      11  86  88 8.794870e-01
## PZA02667.1       0      12  63 110 3.524515e-04
## PZA03324.1       0      17  70  98 3.075356e-02
## PZA00902.1       0      12  70 103 1.210928e-02
## PZA01588.1       0      29  79  77 8.727801e-01
## PZA01787.1       0       9  84  92 5.464936e-01
## PZA03226.3       0      12  81  92 4.029780e-01
## PZA03416.7       0       7  97  81 2.304305e-01
## PZA01799.1.2     0       8 101  76 6.022878e-02
## PZA00189.23      0       8  89  88 9.400837e-01
## PZD00038.2       0      39  81  65 1.854468e-01
## PZA03713.1       0      15  82  88 6.453877e-01
## PZA02984.10      0       9  85  91 6.510766e-01
## PHM5098.25       0      36  84  65 1.195796e-01
## PZA01810.2       0      10  99  76 8.209871e-02
## PZA02619.1       0       8  88  89 9.400837e-01
## PHM565.31        0      27  76  82 6.331240e-01
## PZA00981.3       0      33  68  84 1.943659e-01
## PZA02032.1       0       6  97  82 2.622229e-01
## PZA00517.7       0      12  81  92 4.029780e-01
## PZA01029.1       0      15  81  89 5.394982e-01
## PZA01068.1       0      14  92  79 3.201572e-01
## PZA02742.1       0      10  90  85 7.054570e-01
## PZA02964.7       0      22  78  85 5.834981e-01
## PZA02939.10      0      12  88  85 8.195796e-01
## PHM3925.79       0       3  90  92 8.821456e-01
## PZA02496.1       0      22  88  75 3.085646e-01
## PZA01807.1       0       4  95  86 5.035180e-01
## PHM4757.14       0      11  86  88 8.794870e-01
## PZA03120.1       0      12  86  87 9.393964e-01
## PZA02114.1       0      12  95  78 1.961889e-01
## PZA02197.1       0       8  85  92 5.987824e-01
## PHM5822.15       0      13  68 104 6.051564e-03
## PZB00114.1       0       8  92  85 5.987824e-01
## PZA00731.7       0       8  82  95 3.284999e-01
## PZA00528.1       0       6  88  91 8.225779e-01
## PZA00865.1       0      16  86  83 8.174941e-01
## PZA01566.1       0      10  99  76 8.209871e-02
## PZA03624.1       0      16  78  91 3.173105e-01
## PZA02134.3       0      11  89  85 7.617076e-01
## PHM4955.12       0       7  78 100 9.915384e-02
## PZA00339.4       0       8  93  84 4.987350e-01
## PZB01358.1       0      19  78  88 4.376601e-01
## PZB01957.1       0      11  85  89 7.617076e-01
## PZA00108.4       0      28  65  92 3.117478e-02
## PZB01112.1       0      19  72  94 8.772337e-02
## PZA02271.1       0      13  88  84 7.603683e-01
## PZA02509.15      0       2  87  96 5.058592e-01
## PZA02427.1       0      24  84  77 5.811695e-01
## PZA02207.1       0      33  68  84 1.943659e-01
## PZA00652.17      0      13  69 103 9.528791e-03
## PZA02090.1       0      14  94  77 1.935933e-01
## PZA02957.5       0       8  93  84 4.987350e-01
## PZA01422.3       0       8  72 105 1.312233e-02
## PZA00357.19      0      14  81  90 4.912971e-01
## PZA00460.3.8     0       8  80  97 2.013206e-01
## PZA02279.1       0      13  89  83 6.473148e-01
## PHM3465.6        0       8  89  88 9.400837e-01
## PZA03027.12      0      16  85  84 9.386847e-01
## PZA02018.1       0      12  87  86 9.393964e-01
## PHM3637.14       0      10  95  80 2.568393e-01
## PZA00707.9       0       9  89  87 8.801685e-01
## PZA01144.1       0      13  85  87 8.787937e-01
## PZA01964.29      0      51  64  70 6.042343e-01
## PZA00726.8.10    0      10  62 113 1.156173e-04
## PZA00664.3       0      16  78  91 3.173105e-01
## PZA02186.1       0      13  81  91 4.457659e-01
## PZA03613.1       0      10  94  81 3.257514e-01
## PZA03465.1       0      16  96  73 7.685537e-02
## PZA03231.1       0      23  62 100 2.830578e-03
## PHM2438.28       0       4  87  94 6.028504e-01
## PZB01009.2.1     0      12  84  89 7.038393e-01
## PHM17210.5       0       7  76 102 5.132142e-02
## PHM4942.12       0       9  79  97 1.748444e-01
## PZA01233.1       0      10  85  90 7.054570e-01
## PHM1834.47       0       9  85  91 6.510766e-01
## PZA01374.1       0       9  89  87 8.801685e-01
## PZA00856.2       0       8  94  83 4.083444e-01
## PZA03092.7       0      12  90  83 5.945874e-01
## PZA01883.2       0      64  59  62 7.850629e-01
## PZA00623.3       0       9  92  84 5.464936e-01
## PZA02516.1       0      14  80  91 4.002409e-01
## PZA02680.1       0       3  83  99 2.356227e-01
## PZA02209.2       0      17  70  98 3.075356e-02
## PHM5435.25       0      13  86  86 1.000000e+00
## PHM16605.19      0      17  83  85 8.773706e-01
## PZA00925.2       0      10  82  93 4.056789e-01
## PZA02219.2       0      10  84  91 5.967012e-01
## PZA01470.1       0       8  93  84 4.987350e-01
## PHM7953.11       0       9  84  92 5.464936e-01
## PZA02385.6       0      15  70 100 2.139757e-02
## PZA00613.22      0      30  64  91 3.010606e-02
## PZB00895.1       0      29  85  71 2.623317e-01
## PZA03200.2       0      17  91  77 2.800872e-01
## PZA00030.11      0      23  88  74 2.713566e-01
## PZA01321.1       0      12  90  83 5.945874e-01
## PZA01254.2       0      16  93  76 1.909777e-01
## PHM13619.5       0      10  85  90 7.054570e-01
## PZA02614.2       0      10 100  75 5.878172e-02
## PZA02012.7       0       7  85  93 5.487559e-01
## PZA02014.3       0       9  85  91 6.510766e-01
## PHM3668.12       0      10  84  91 5.967012e-01
## PZA02528.1       0      12  84  89 7.038393e-01
## PZB01658.1       0      37  69  79 4.110798e-01
## PZA01885.2       0       7  86  92 6.529131e-01
## PZA03119.1       0      12  88  85 8.195796e-01
## PZA03322.5.3     0      19  95  71 6.249586e-02
## PHM4080.15       0      32  68  85 1.693273e-01
## PZA01196.2       0      20  86  79 5.857884e-01
## PZA03491.1       0      14  84  87 8.185458e-01
## PZA00683.4       0      28  65  92 3.117478e-02
## PZA02545.1       0      11  82  92 4.483923e-01
## PHM2551.31       0      13  84  88 7.603683e-01
## PZA02148.1       0      16  82  87 7.005224e-01
## PZA00828.2       0       5  77 103 5.263230e-02
## ZHD1.1           0      10  90  85 7.054570e-01
## PZB01308.1       0      15  83  87 7.590063e-01
## PZD00030.2       0      10  95  80 2.568393e-01
## PZA01257.1       0       9  93  83 4.509823e-01
## PZA01342.2       0      13  85  87 8.787937e-01
## PZA00866.2       0       9  85  91 6.510766e-01
## PZA03205.1       0       9  99  77 9.725443e-02
## PZA00152.1       0      14  81  90 4.912971e-01
## PZA01715.1.2     0       5  86  94 5.509850e-01
## PZA01677.1       0      14  84  87 8.185458e-01
## PZA00071.2       0       4  88  93 7.101556e-01
## PZA02027.1       0      13  73  99 4.742539e-02
## PZA02606.1       0       9  85  91 6.510766e-01
## PZA03054.5       0       9  88  88 1.000000e+00
## PZA00172.12      0       9  75 101 5.001640e-02
## PZA02727.1       0       6  91  88 8.225779e-01
## PZA03604.1       0      12  82  91 4.938127e-01
## PZA02094.9       0      23  81  81 1.000000e+00
## PZA01438.1       0       9  95  81 2.912928e-01
## PZA03612.2.1     0       8  84  93 4.987350e-01
## PZA02133.10      0      10  71 104 1.261114e-02
## PZB00772.7       0      22  78  85 5.834981e-01
## PZA02613.1       0       8  83  94 4.083444e-01
## PZA01542.1       0      13  79  93 2.857506e-01
## PZA02058.1       0       7  96  82 2.940197e-01
## PHM4748.16       0      12  88  85 8.195796e-01
## PZA02566.1       0      16  84  85 9.386847e-01
## PZA01591.1       0      16  80  89 4.887441e-01
## PZB00008.1       0      29  85  71 2.623317e-01
## PZA00529.4       0      10 102  73 2.836551e-02
## PZA01895.1       0       4  88  93 7.101556e-01
## PZA02872.1       0      10  76  99 8.209871e-02
## PZA01414.1       0      13  90  82 5.418656e-01
## PZA00363.7       0      10  84  91 5.967012e-01
## PZA03321.4       0       6  90  89 9.404188e-01
## PZA02478.7       0      20  79  86 5.857884e-01
## PZA00276.18      0      30  83  72 3.769439e-01
## PZA03020.8       0      10  92  83 4.962917e-01
## PZA00963.3       0      18  53 114 2.354719e-06
## PZA01265.1       0      13  63 109 4.523943e-04
## PZA01861.1       0      11  80  94 2.885367e-01
## PZA03047.12      0       9  89  87 8.801685e-01
## PZB01899.1       0      13  81  91 4.457659e-01
## PZA02774.1       0      14  96  75 1.082937e-01
## PZA01001.2       0      39  76  70 6.194969e-01
## PHM9807.9        0      11  95  79 2.251463e-01
## PHM16125.47      0       9  85  91 6.510766e-01
## PZA00130.9       0      13  87  85 8.787937e-01
## PZA02722.1       0      14  85  86 9.390437e-01
## PZA03154.4       0       9  82  94 3.657123e-01
## PHM5181.10       0      24  91  70 9.791804e-02
## PZA02372.1       0       9  89  87 8.801685e-01
## PZA00067.10      0      13  75  97 9.344782e-02
## PZA01477.3       0      12  89  84 7.038393e-01
## PZA02456.1       0       3  86  96 4.585423e-01
## PZA02221.20      0       3  89  93 7.668485e-01
## PZA03247.1       0      12  62 111 1.950050e-04
## PZA02320.1       0      10  86  89 8.205958e-01
## PZA00962.1       0      12  88  85 8.195796e-01
## PZA00566.5       0       7  85  93 5.487559e-01
## PZA00088.3       0       3 101  81 1.382077e-01
## PZA00413.20.18   0       6  88  91 8.225779e-01
## PZA00466.1       0       8 101  76 6.022878e-02
## PZA00445.22      0      10  63 112 2.121829e-04
## PHM4905.6        0      21  79  85 6.394119e-01
## PZA01297.1       0       9  91  85 6.510766e-01
## PZA02633.4       0      17  70  98 3.075356e-02
## PHM3978.104      0       8  88  89 9.400837e-01
## PHM13681.12      0      30  78  77 9.359812e-01
## PZA03301.2       0      32  74  79 6.860465e-01
## PHM13687.14      0      12  83  90 5.945874e-01
## PHM4604.18       0       5  86  94 5.509850e-01
## PZA00770.1       0       8  88  89 9.400837e-01
## PZA00015.5       0      14  81  90 4.912971e-01
## PHM18195.6       0       7  92  86 6.529131e-01
## PZA00934.2       0       9  84  92 5.464936e-01
## PZA02070.1       0      16  93  76 1.909777e-01
## PZA02296.1       0      14  88  83 7.021947e-01
## PZA01741.1       0      11  86  88 8.794870e-01
## PHM2885.31       0      10  81  94 3.257514e-01
## PZA02040.2       0      14  71 100 2.657629e-02
## PZA01427.1       0      27  76  82 6.331240e-01
## PZA01019.1       0      32  76  77 9.355651e-01
## PZA03744.1       0      17  81  87 6.434288e-01
## PZA02398.2       0      10  90  85 7.054570e-01
## PZA00766.1       0       8  88  89 9.400837e-01
## PZA03363.1       0      11  86  88 8.794870e-01
## PZA02212.1       0       6  81  98 2.038569e-01
## PZA02520.1       0       7  93  85 5.487559e-01
## PZA01313.2       0      64  59  62 7.850629e-01
## PZA00980.1       0      66  22  97 6.188613e-12
## PZA00987.1       0      17  71  97 4.486227e-02
## PZA02449.13      0       9  84  92 5.464936e-01
## PHM8909.12       0      50  67  68 9.314137e-01
## PZA01284.6       0      12  90  83 5.945874e-01
## PZA02167.2       0      39  76  70 6.194969e-01
## PZA03536.1       0      13  72 100 3.276265e-02
## PHM15623.10      0      22  81  82 9.375687e-01
## PZA03409.1       0      12  73 100 4.009470e-02
## PZA00636.7       0      16 100  69 1.709699e-02
## PZA02683.1       0      18  85  82 8.164239e-01
## PZA02436.1       0      49  66  70 7.316006e-01
## PZB01110.6       0      18  84  83 9.383194e-01
## PZA02015.11      0      67  14 104 1.179389e-16
## PZA02383.1       0      17  70  98 3.075356e-02
## PZA02373.1       0      26  78  81 8.119467e-01
## PZA00057.2       0       8  78  99 1.144607e-01
## PZA03049.24      0      12  75  98 8.035022e-02
## PZA01652.1       0      23  81  81 1.000000e+00
## PZA00219.7       0      13  85  87 8.787937e-01
## PZA01457.1       0      15  81  89 5.394982e-01
## PHM4621.57       0      15  77  93 2.197685e-01
## PHM3691.18       0      33  68  84 1.943659e-01
## PZA01765.1       0      12  88  85 8.195796e-01
## PZA02589.1       0      11  89  85 7.617076e-01
## PZB00872.3       0      14  92  79 3.201572e-01
## PZA00104.1       0      12  64 109 6.232560e-04
## PHM3034.3        0      14  81  90 4.912971e-01
## PZA03074.27      0       5  89  91 8.814975e-01
## PZA00740.3       0       8  83  94 4.083444e-01
## PZA02029.21      0      15  88  82 6.453877e-01
## PZA01396.1       0      12  77  96 1.485862e-01
## PZA00944.1.2     0      22  93  70 7.162426e-02
## PHM12625.18      0       7  92  86 6.529131e-01
## PZA03048.18      0       4  69 112 1.392675e-03
## PZA02676.2       0      14  76  95 1.462331e-01
## PZA02376.1       0      18  86  81 6.988216e-01
## PZA02111.1       0      10  84  91 5.967012e-01
## PZA02731.1       0       9  85  91 6.510766e-01
## PZA00942.2       0      14  83  88 7.021947e-01
## PZA00090.1       0       5  88  92 7.655945e-01
## PZA03196.1       0       9  85  91 6.510766e-01
## PZA00985.1       0      10  89  86 8.205958e-01
## PZA00309.1       0       3  97  85 3.737349e-01
## umc128.2         0      16  78  91 3.173105e-01
## PZA01983.1       0       5 105  75 2.534732e-02
## PZA00112.5       0      10  91  84 5.967012e-01
## PZA01935.10      0      13  68 104 6.051564e-03
## PZA02411.3       0      16  71  98 3.780866e-02
## PZA00485.2       0      25  79  81 8.743671e-01
## PZA03036.6       0       8  85  92 5.987824e-01
## PZA03461.1       0      13  80  92 3.601961e-01
## PZA03639.1       0      13  84  88 7.603683e-01
## PZA01106.3       0      11  62 112 1.503503e-04
## PHM9695.8        0      12  71 102 1.842889e-02
## PZB00063.1       0      29  85  71 2.623317e-01
## PZA02170.1       0      10  90  85 7.054570e-01
## PZA02421.1       0      11  99  75 6.884504e-02
## PZA02423.1       0       3 101  81 1.382077e-01
## PHM2423.33       0       5  96  84 3.710934e-01
## PZA01410.1       0      17  71  97 4.486227e-02
## PZA01804.1       0      10  77  98 1.124106e-01
## PZD00015.5       0       5  86  94 5.509850e-01
## PZB00718.5       0      16  81  88 5.902585e-01
## PZA03723.1       0      11  86  88 8.794870e-01
## PZA00619.3       0      43  78  64 2.400532e-01
## zfl2.9           0      13  72 100 3.276265e-02
## PZB02058.1       0      29  76  80 7.487740e-01
## PZA00522.12.7    0      10  78  97 1.509270e-01
## PHM10525.9.11    0       9  84  92 5.464936e-01
## PZA03240.1.2     0      15  95  75 1.250469e-01
## PZA01905.12      0      31  82  72 4.203449e-01
## PHM4531.46       0      24  79  82 8.130966e-01
## PZA00048.1       0      14  85  86 9.390437e-01
## PZA01736.1       0      16  81  88 5.902585e-01
## PZA02525.1       0      19  72  94 8.772337e-02
## PZA02292.1       0      13  92  80 3.601961e-01
## PHM12859.7       0      22  87  76 3.889151e-01
## PZA02356.7       0      17  70  98 3.075356e-02
## PZA01951.1       0      12  74  99 5.733938e-02
## PZB01013.1       0       4  91  90 9.407483e-01
## PZA02824.4       0      12  84  89 7.038393e-01
## PZA03064.6       0      17  83  85 8.773706e-01
## PZA02138.1       0      12  72 101 2.746608e-02
## PZB00605.1       0      13  91  81 4.457659e-01
## PHM15449.10      0       6  88  91 8.225779e-01
## PZA01246.1       0       9  91  85 6.510766e-01
## PHM5296.6        0      17  70  98 3.075356e-02
## PZA00261.6       0      20  71  94 7.336593e-02
## PZA01455.1       0      12  86  87 9.393964e-01
## PHM3055.9        0      28  75  82 5.763932e-01
## PZA00941.2       0       9 104  72 1.586133e-02
## PHM890.20        0       5  78 102 7.363827e-02
## PZA03470.1       0      10  83  92 4.962917e-01
## PZA01456.2       0      11  82  92 4.483923e-01
## PZA03564.1       0      13  62 110 2.522490e-04
## PZA00218.1       0      11  60 114 4.244896e-05
## PZA00783.1       0       7  81  97 2.304305e-01
## PZA00191.5       0      31  88  66 7.626011e-02
## PZB01227.6       0      50  71  64 5.468653e-01
## umc13.1          0      13  92  80 3.601961e-01
## PHM4204.69       0       9  93  83 4.509823e-01
## PZA01336.1       0       9  93  83 4.509823e-01
## PZA02616.1       0      14  82  89 5.924401e-01
## PZD00016.4       0       5  86  94 5.509850e-01
## PZA03596.1       0      11  82  92 4.483923e-01
## PZA00148.3       0      18  74  93 1.414902e-01
## PZA01228.2       0      16  81  88 5.902585e-01
## PZA02396.14      0      16  80  89 4.887441e-01
## PZA03644.1       0      21  83  81 8.758961e-01
## PZA01497.1       0       8  87  90 8.215951e-01
## PHM4203.11       0       9  87  89 8.801685e-01
## PZA02823.1       0       9  78  98 1.316680e-01
## PZA00892.5       0      11  83  91 5.441971e-01
## PZA02223.2       0      14  88  83 7.021947e-01
## PZA00294.22      0      14  94  77 1.935933e-01
## PZA03168.5       0      14  92  79 3.201572e-01
## PZA01186.1       0      21  82  82 1.000000e+00
## PZA02751.1       0      18  69  98 2.482678e-02
## PZA03235.1       0      16  79  90 3.974669e-01
## PZA02681.8       0      10  72 103 1.910992e-02
## PZA00991.2       0       8  92  85 5.987824e-01
## PZA02077.1       0       6  87  92 7.086145e-01
## PZA03659.1       0      10  86  89 8.205958e-01
## PZA02763.1       0      24  89  72 1.803144e-01
## PZB01103.2       0      21  78  86 5.321712e-01
## PZA01575.1       0      17  70  98 3.075356e-02
## PZA03344.2       0      14  82  89 5.924401e-01
## PZA01030.1       0      12  89  84 7.038393e-01
## PZA01589.2       0      17  79  89 4.404007e-01
## PZA00910.1       0       6  87  92 7.086145e-01
## PZA00235.9       0       9  92  84 5.464936e-01
## PZA02328.5       0      14  81  90 4.912971e-01
## PZA00527.10      0      31  71  83 3.335503e-01
## PZA02095.10      0      64  59  62 7.850629e-01
## PZA01819.1       0       9  83  93 4.509823e-01
## PZA00814.1       0      11  86  88 8.794870e-01
## PZB01301.5       0       8  87  90 8.215951e-01
## PZA02818.6       0      16  73  96 7.685537e-02
## PZA01462.1       0      49  66  70 7.316006e-01
## PZB01111.8       0       9  87  89 8.801685e-01
## PHM112.8         0       4  95  86 5.035180e-01
## PZA03602.1       0       3  86  96 4.585423e-01
## PZA02854.13      0      13  81  91 4.457659e-01
## PZA01079.1       0       9  77  99 9.725443e-02
## PHM5468.25       0       8  85  92 5.987824e-01
## PZA01122.1       0      12  72 101 2.746608e-02
## PZA02098.2       0       9  95  81 2.912928e-01
## PHM5502.31       0      11  89  85 7.617076e-01
## wx1.1            0       8  91  86 7.070485e-01
## vdac1a.1         0      21  83  81 8.758961e-01
## PZA00139.4       0      28  62  95 8.446338e-03
## PZA01714.1       0      16  76  93 1.909777e-01
## PZA03274.4       0      12  81  92 4.029780e-01
## PZB01233.1       0      10  71 104 1.261114e-02
## PZA00908.2       0       8  88  89 9.400837e-01
## PZA02352.1       0      10  79  96 1.987646e-01
## PZA01751.2       0      13  61 111 1.375881e-04
## PHM537.22        0      10  90  85 7.054570e-01
## PZA00062.4       0      10  94  81 3.257514e-01
## PZA03598.1       0      10  94  81 3.257514e-01
## PZA00894.7       0      13  86  86 1.000000e+00
## PHM3631.47       0      64  59  62 7.850629e-01
## PZA03404.1       0      24  78  83 6.935403e-01
## PZA03183.5       0      15  91  79 3.573857e-01
## PHM15445.25      0      12  86  87 9.393964e-01
## PZA02982.7       0       8  72 105 1.312233e-02
## PZA02378.7       0       8  93  84 4.987350e-01
## PZA02044.1       0       8  96  81 2.595442e-01
## PZA02426.1       0      18  69  98 2.482678e-02
## PZA02585.2       0      31  94  60 6.147694e-03
## PHM9914.11       0      21  69  95 4.233023e-02
## PZA00538.18.15   0      12  81  92 4.029780e-01
## PZA01690.7       0       8  81  96 2.595442e-01
## PZA00279.2       0      12  88  85 8.195796e-01
## PZA02733.1       0      32  70  83 2.932642e-01
## PZA02060.1       0      13  62 110 2.522490e-04
## csu1138.3.4      0      19  90  76 2.772089e-01
## PZA00695.3       0      13  93  79 2.857506e-01
## PZA02985.5       0       9  94  82 3.657123e-01
## PZA03194.1       0      32  76  77 9.355651e-01
## PZA00429.1       0      10  88  87 9.397430e-01
## PZA00635.7       0      13  89  83 6.473148e-01
## PZA00380.10      0      11  86  88 8.794870e-01
## PZA03551.1       0      11  84  90 6.492108e-01
## PZB00093.7       0      15  68 102 9.115787e-03
## PZA00975.1       0      12  72 101 2.746608e-02
## PZA00473.5       0      14  82  89 5.924401e-01
## PZA01755.1       0      11  85  89 7.617076e-01
## PHM3896.9        0      10  84  91 5.967012e-01
## PHM15899.9       0      13  86  86 1.000000e+00
## PZA02284.1       0       9  82  94 3.657123e-01
## PZA00029.17      0      12  86  87 9.393964e-01
## PZA03568.1       0      16  84  85 9.386847e-01
## PHM2350.17       0      20  86  79 5.857884e-01
## PZA00444.1       0       7  92  86 6.529131e-01
## PZA01960.1       0       8  94  83 4.083444e-01
## PZA00717.15      0       9  93  83 4.509823e-01
## PZA01763.2       0      14  71 100 2.657629e-02
## PZA02239.12      0      31  82  72 4.203449e-01
## PZA01363.2       0      16  88  81 5.902585e-01
## PZA03001.15      0      35  71  79 5.136291e-01
## PZA00368.1       0      23  77  85 5.296507e-01
## PZA02648.2       0       8  86  91 7.070485e-01
## PZA00803.3       0      11  83  91 5.441971e-01
## PZA02626.1       0      12  86  87 9.393964e-01
## PZA02653.12      0      18  91  76 2.457497e-01
## sh2.21           0       9  82  94 3.657123e-01
## PZA03012.7       0       9  88  88 1.000000e+00
## PZA01642.1       0      10  84  91 5.967012e-01
## PZA02080.1       0      24  78  83 6.935403e-01
## PZA00499.3       0      33  68  84 1.943659e-01
## PZA00271.1       0      11  89  85 7.617076e-01
## PZA02462.1       0      16  91  78 3.173105e-01
## PZA00379.2       0       9  93  83 4.509823e-01
## PZA02981.2       0      25  68  92 5.777957e-02
## PZA00440.15.1    0       8  89  88 9.400837e-01
## PZA02281.3       0       4  88  93 7.101556e-01
## PZA01680.3       0      67  14 104 1.179389e-16
## PZA01259.1       0      67  14 104 1.179389e-16
## PZA00266.7       0      17  85  83 8.773706e-01
## PHM5665.26       0      10 103  72 1.910992e-02
## PZA00836.1       0      67  14 104 1.179389e-16
## PZA03243.2       0      13  90  82 5.418656e-01
## PZA03647.1       0      24  76  85 4.781387e-01
## PHM5572.19       0      11  62 112 1.503503e-04
## PZA01473.1       0       9  88  88 1.000000e+00
## PZB00221.3       0       8  85  92 5.987824e-01
## PZA03135.1       0       8  88  89 9.400837e-01
## PHM4880.179      0      13  87  85 8.787937e-01
## PZA03032.19      0       7  82  96 2.940197e-01
## PZA01349.2       0      19  72  94 8.772337e-02
## PHM14475.7       0      14  85  86 9.390437e-01
## PZA01357.2       0      10  79  96 1.987646e-01
## PZA00224.4       0      25  76  84 5.270893e-01
## PZA01096.1       0       9  81  95 2.912928e-01
## PZA01925.1       0      17  86  82 7.576207e-01
## PZA01281.2       0      12  81  92 4.029780e-01
## PHM4348.16       0      11 101  73 3.378114e-02
## PZA00801.1       0      33  68  84 1.943659e-01
## PZA02702.1       0       6  97  82 2.622229e-01
## PZA00637.6       0      21  83  81 8.758961e-01
## PZA02753.1       0      25  86  74 3.427817e-01
## PHM2672.19       0      13  85  87 8.787937e-01
## PHM9162.135      0       9  84  92 5.464936e-01
## PZA02397.1       0      21  79  85 6.394119e-01
## PHM5019.59       0       4  88  93 7.101556e-01
## PHM5817.15       0       4  75 106 2.121075e-02
## PZA00889.2       0      12  86  87 9.393964e-01
## PHM15868.56      0       8  83  94 4.083444e-01
## PZB01109.1       0      16  81  88 5.902585e-01
## PHM7898.10       0      12  88  85 8.195796e-01
## PZA00545.26      0      40  27 118 4.120326e-14
## PZB00054.3       0      17  91  77 2.800872e-01
## PZA02019.1       0       9  87  89 8.801685e-01
## PZA01946.7       0       9  81  95 2.912928e-01
## PZA03254.1       0      10  61 114 6.164510e-05
## PZA00821.1       0      12  83  90 5.945874e-01
## PZA02266.3       0       3  86  96 4.585423e-01
## PZA01039.1       0      10  85  90 7.054570e-01
## PZA00860.1       0       8  95  82 3.284999e-01
## PZA01991.3       0       5  87  93 6.547208e-01
## PZA02767.1       0      11  60 114 4.244896e-05
## PZA00832.1       0       8  89  88 9.400837e-01
## PZA01884.1       0      15  82  88 6.453877e-01
## PZB00409.6       0      14  84  87 8.185458e-01
## PZA03452.6       0      17  70  98 3.075356e-02
## PHM3171.5        0      14  76  95 1.462331e-01
## PHM15961.13      0       2  90  93 8.244958e-01
## PZA01451.1       0       7  88  90 8.808385e-01
## PZA00405.7.6     0      10  84  91 5.967012e-01
## PZA00316.10      0      15  84  86 8.780884e-01
## PZA02554.1       0       1  92  92 1.000000e+00
## PZA02878.13      0      10  83  92 4.962917e-01
## PZA02269.3.4     0      14  82  89 5.924401e-01
## PZA03146.4       0       9  79  97 1.748444e-01
## PZA00933.3       0      18  82  85 8.164239e-01
## PZB00648.5       0      21  81  83 8.758961e-01
## PHM4647.8        0      33  68  84 1.943659e-01
## PZA01352.5       0       6  89  90 9.404188e-01
## PZA00581.3       0      14  86  85 9.390437e-01
## PZA01210.1.2     0      17  82  86 7.576207e-01
## PZA03578.1       0      15  83  87 7.590063e-01
## PZA01271.1       0      10  94  81 3.257514e-01
## PHM5480.17       0      32  76  77 9.355651e-01
## PZD00072.2       0       9  86  90 7.630246e-01
## PZA01301.1       0      16  88  81 5.902585e-01
## PZA02187.1.2     0      20  79  86 5.857884e-01
## PZA03081.1       0      10 100  75 5.878172e-02
## PZA02033.1       0       9  87  89 8.801685e-01
## PHM4818.15       0      12  84  89 7.038393e-01
## PZA00255.14      0      16  71  98 3.780866e-02
## PZA00007.1       0      17  85  83 8.773706e-01
## PZA03698.1       0      15  86  84 8.780884e-01
## PZA00590.1       0      16  79  90 3.974669e-01
## PZA00400.3       0       9  90  86 7.630246e-01
## PHM4303.16       0       7  86  92 6.529131e-01
## PHM1745.16       0      14  78  93 2.513491e-01
## PZA00256.27      0      32  68  85 1.693273e-01
## PHM2749.10       0      15  82  88 6.453877e-01
## PZB01115.3       0      16  75  94 1.438677e-01
## PZA01926.1       0       9  81  95 2.912928e-01
## PZA00308.24      0      15  82  88 6.453877e-01
## PZA03451.5       0      18  74  93 1.414902e-01
## PZB00540.3       0       9  91  85 6.510766e-01
## PHM4165.14       0      22  71  92 1.000014e-01
## PZA00245.20      0      18  84  83 9.383194e-01
## PZA00920.1       0       7  78 100 9.915384e-02
## PHM3137.17       0      13  91  81 4.457659e-01
## PZA00006.17      0      13  84  88 7.603683e-01
## PZA01608.1       0      15  74  96 9.154127e-02
## PZA03063.21      0      10  89  86 8.205958e-01
## PHM12830.14      0      11  84  90 6.492108e-01
## PZB00414.2       0      15  84  86 8.780884e-01
## PZA00381.4       0      17  81  87 6.434288e-01
## PHM5599.20       0      31  94  60 6.147694e-03
## PZA00805.1       0      10  78  97 1.509270e-01
## PZA02992.15      0       7  73 105 1.646231e-02
## PHM1511.14       0      10  71 104 1.261114e-02
## PZA01426.1       0       4  91  90 9.407483e-01
## PZA00111.10      0       8  83  94 4.083444e-01
## PZA03265.3       0      21  79  85 6.394119e-01
## PZD00055.1       0       8  85  92 5.987824e-01
## PHM12706.14      0      11  84  90 6.492108e-01
## PZA01570.1       0       6  94  85 5.011435e-01
## PZA00947.1       0      12  81  92 4.029780e-01
## PZB01017.1       0      22  70  93 7.162426e-02
## PZA00285.3       0       7 100  78 9.915384e-02
## PZA02820.17      0      14  63 108 5.790632e-04
## PZA02147.1       0       9  74 102 3.480848e-02
## PZA00131.15      0       6  92  87 7.086145e-01
## PZA03102.9       0      13  85  87 8.787937e-01
## PZA02673.1       0      16  82  87 7.005224e-01
## PZA02358.1       0      46  56  83 2.201493e-02
## PZA03579.1       0      12  85  88 8.195796e-01
## PZA03606.1       0      12  82  91 4.938127e-01
## PZA00755.2       0      34  69  82 2.900896e-01
## PZA00348.11      0      10  87  88 9.397430e-01
## PZA00824.2       0      10  84  91 5.967012e-01
## PZA00818.1       0       5 104  76 3.688843e-02
## PZA03637.1       0      13  84  88 7.603683e-01
## PZB01569.7       0      15  85  85 1.000000e+00
## PZB02044.1       0       8  77 100 8.384743e-02
## PZA02450.1       0      21  93  71 8.581278e-02
## PZA02195.1       0      13  89  83 6.473148e-01
## PHM5798.39       0      22  71  92 1.000014e-01
## PZA02035.5       0      10  91  84 5.967012e-01
## PHM3765.7        0      64  59  62 7.850629e-01
## PHM15871.11      0       9  79  97 1.748444e-01
## PZD00054.1       0      13  79  93 2.857506e-01
## PZA02365.7       0       9  86  90 7.630246e-01
## PZA00515.10      0      21  83  81 8.758961e-01
## PZA00658.21      0       8  78  99 1.144607e-01
## PHM1899.157      0      17  70  98 3.075356e-02
## PHM4586.12       0       9  94  82 3.657123e-01
## PZA00610.16      0       8  91  86 7.070485e-01
## PZA01142.4       0      13  67 105 3.761823e-03
## PZA01523.1       0       9  86  90 7.630246e-01
## PZA02264.5       0      10  71 104 1.261114e-02
## PHM12749.13      0      15  86  84 8.780884e-01
## PZA03459.1       0      12  74  99 5.733938e-02
## PZA02641.2       0      14  72  99 3.894746e-02
## PZA00300.14      0      16  71  98 3.780866e-02
## PHM9241.13       0       9  84  92 5.464936e-01
## PZA01681.1       0      12  89  84 7.038393e-01
## PZA01371.1       0      17  86  82 7.576207e-01
## PZA00427.3       0       8  86  91 7.070485e-01
## csu1171.2        0       7  88  90 8.808385e-01
## PZA01688.3       0      15  83  87 7.590063e-01
## PZA02388.1       0       2  98  85 3.365584e-01
## PZA01290.1       0      15  82  88 6.453877e-01
## PHM3301.28       0       8  72 105 1.312233e-02
## PHM8527.2        0      12  71 102 1.842889e-02
## PZA03167.5       0      67  14 104 1.179389e-16
## PHM3155.14       0      24  82  79 8.130966e-01
## PZA02750.3       0      24  89  72 1.803144e-01
## PHM18513.156     0      18  78  89 3.946552e-01
## PZA01028.2       0      10  90  85 7.054570e-01
## PZB02179.1       0       5  77 103 5.263230e-02
## PZA00840.1       0       8  86  91 7.070485e-01
## PZA03057.3       0      10  82  93 4.056789e-01
## PZA00132.17      0      13  83  89 6.473148e-01
## PZA00795.1       0       8  85  92 5.987824e-01
## PZA03670.1       0      10  84  91 5.967012e-01
## PZA03651.1       0      22  81  82 9.375687e-01
## PZB01042.2       0       8  90  87 8.215951e-01
## PZA02643.1       0       8  79  98 1.532552e-01
## PHM13440.13      0      10  71 104 1.261114e-02
## PZA02969.9       0      14  87  84 8.185458e-01
## PHM9374.5        0      11  95  79 2.251463e-01
## PHM1675.29       0      12  76  97 1.103548e-01
## PZB01062.3       0      11  95  79 2.251463e-01
## PZA03178.1       0       3  74 108 1.172723e-02
## PZA03733.1       0      35  67  83 1.914184e-01
## PZA02329.2       0      21  78  86 5.321712e-01
## PZA01934.6       0       7  78 100 9.915384e-02
## PZA01976.9       0      12  94  79 2.541077e-01
## PZA01735.1       0      34  69  82 2.900896e-01
## PZA01468.1       0      15  87  83 7.590063e-01
## PZA03714.1       0      19  69  97 2.976365e-02
## PZA01802.3       0      11  89  85 7.617076e-01
## PZA00418.2       0      12  84  89 7.038393e-01
## PZA03671.1       0      10  84  91 5.967012e-01
## PHM2478.22       0       8  79  98 1.532552e-01
## PZA00352.23      0      18  69  98 2.482678e-02
## PZA03743.1       0      17  81  87 6.434288e-01
## PZA03583.1       0      16  76  93 1.909777e-01
## PZA00365.2       0       4  75 106 2.121075e-02
## PHM15278.6       0      15  86  84 8.780884e-01
## PZA02705.1       0      14  60 111 9.616588e-05
## PHM448.23        0       8  88  89 9.400837e-01
## PHM3512.186      0      13  60 112 7.340739e-05
## PZB01647.1       0      35  71  79 5.136291e-01
## PZA00432.4       0       4  95  86 5.035180e-01
## PZA03203.2       0      13  64 108 7.937401e-04
## PHM4711.14       0       7  87  91 7.643200e-01
## PZA01972.14      0      10  88  87 9.397430e-01
## PZA00382.17      0      15  81  89 5.394982e-01
## PZA03692.1       0      21  83  81 8.758961e-01
## PZA00904.1       0      51  64  70 6.042343e-01
## PZA01476.1       0      13  95  77 1.699118e-01
## PZA03058.22.21   0      26  84  75 4.753840e-01
## PZA00344.10      0       8 105  72 1.312233e-02
## PZA00337.4       0      11  87  87 1.000000e+00
## PHM3922.32       0      14  82  89 5.924401e-01
## PZA02381.1       0       4  91  90 9.407483e-01
## PZA01820.1       0      15  97  73 6.566319e-02
## PZA01062.1       0      14  80  91 4.002409e-01
## PZA02686.1       0      12  86  87 9.393964e-01
## PZA00297.2       0      10  87  88 9.397430e-01
## PHM934.19        0      10  85  90 7.054570e-01
## PZA02174.2       0      40  76  69 5.610259e-01
## PHM1766.1        0      21  77  87 4.348797e-01
## PZA02665.2       0      26  76  83 5.788016e-01
## PHM4145.18       0       9  96  80 2.278000e-01
## PHM15427.11      0      13  61 111 1.375881e-04
## PZA01999.3       0       8  91  86 7.070485e-01
## PZA02550.1       0      13  93  79 2.857506e-01
## PZA01113.1       0      13  86  86 1.000000e+00
## PHM12992.5       0      33  68  84 1.943659e-01
## PZB00014.1       0      11  83  91 5.441971e-01
## PHM1978.111      0      14  83  88 7.021947e-01
## PZB00959.1       0      11  85  89 7.617076e-01
## PZA01600.2       0       5  90  90 1.000000e+00
## PZD00033.3       0      13  84  88 7.603683e-01
## PZA02278.1       0      11  95  79 2.251463e-01
## PZA01230.1       0      10  86  89 8.205958e-01
## PZA01209.1       0      10  94  81 3.257514e-01
## PZA01216.1       0       5  92  88 7.655945e-01
## PZA01447.1       0      12  88  85 8.195796e-01
## PZB00547.3       0       7  92  86 6.529131e-01
## PZA02467.10      0       8  90  87 8.215951e-01
## PZA00505.6       0      12  89  84 7.038393e-01
## PZB00811.1       0      22  81  82 9.375687e-01
## PZA03176.4       0       6  91  88 8.225779e-01
## PHM15331.16      0       9  88  88 1.000000e+00
## PZA02748.3       0      13  84  88 7.603683e-01
table(gt.population_z006$P.value < 0.05)
## 
## FALSE  TRUE 
##   947   159
table(gt.population_z006$P.value < 0.05/totmar(population_Z006))
## 
## FALSE  TRUE 
##  1091    15

Construção do mapa de ligação

Carregar dados e estimar frações de recombinação em pares

Para começar com a análise, carregamos os dados (aqui, estamos usando a população de linha de raça recombinante Z006) usando a função , e estimamos as frações de recombinação de pairwise (e seus respectivos escores lod) usando a função :read.cross()est.rf()

population_Z006 <- read.cross(format="csv", file="population_z006.csv", genotypes=c("0","1","2"), crosstype = "riself")
##  --Read the following data:
##   185  individuals
##   1106  markers
##   29  phenotypes
##  --Cross type: riself

Observe que existem alguns avisos sobre:

1: A exclusão das heterozigotes remanescentes em uma população RIL (code==2 é a mesma do genótipo=1); 2: O fato de que o mapa ainda não é estimado, então o pacote interpreta cada marcador de 10 cM de distância (o que é claramente errado e vamos corrigi-lo).qtl

population_Z006 <- est.rf(cross = population_Z006)
plotRF(population_Z006, col.scheme = "redblue")

A partir da função, notamos que 1106 marcadores ainda não foram atribuídos a grupos de vinculação. Na prática, recomenda-se verificar a falta de dados de marcadores e distorção de segregação antes do agrupamento, mas vamos ignorá-los por uma questão de tempo aqui.plotRF()

Agrupamento de marcadores

Uma vez que tenhamos as estimativas de fração de recombinação de pairwise, podemos tentar ver quais marcadores estão no mesmo grupo de ligação. Para isso, precisamos fornecer a fração máxima de recombinação (argumento) e pontuação mínima de LOD (argumento). Esses valores são fornecidos à função e são usados para ver se dois marcadores estão ligados ou não, evitando falsos positivos. Vamos mostrar de onde esses valores vêm, mas você pode usá-los diretamente em sua análise.max.rfmin.lodformLinkageGroups()

Para , podemos usar algo em torno de 0,38, que é a fração de recombinação máxima de valor de 0,50 quando convertido via função de mapa Kosambi:max.rf

Uma fração de recombinação tão grande quanto 0,50 significa que dois marcadores estão segregando independentemente (ou seja, esses dois marcadores não estão ligados). Veja abaixo:

max.rf <- 0.38
kosambi <- function(r) (1/4)*log((1+(2*r))/(1-(2*r)))
kosambi(r = max.rf)
## [1] 0.4981075

Pois, podemos executar a correção bonferroni no número de testes que temos que realizar para avaliar a vinculação do marcador. O número de testes é o número de pares de marcadores que temos em nossos dados. Como primeiro palpite, temos:min.lod

(M <- totmar(population_Z006))
## [1] 1106
(num.pair <- choose(M, 2))
## [1] 611065
(min.lrt <- qchisq(1-(0.05/num.pair), 1))
## [1] 28.7624
(min.lod <- 0.2172 * min.lrt)
## [1] 6.247193

Agora, é hora de ver quantos grupos de ligação temos. Primeiro, executamos a função apenas para ver como os marcadores são distribuídos ao longo dos grupos de linkagem formados:formLinkageGroups()reorgMarkers = FALSE

lg <- formLinkageGroups(population_Z006, max.rf=max.rf, min.lod=min.lod, reorgMarkers=FALSE)
table(lg[,2])
## 
##   1   2   3   4   5   6   7   8   9  10 
## 175 139 130 127 111 106  85  78  78  77
population_Z006 <- formLinkageGroups(population_Z006, max.rf=0.38, min.lod=6.25, reorgMarkers=TRUE)
plotRF(population_Z006, col.scheme = "redblue")

O mapa de calor mostra marcadores agrupados, mas ainda não ordenados dentro de cada grupo de ligação.

Ordenação de marcadores

Vamos mostrar duas maneiras de ordenar marcadores. A primeira maneira usa a função por R/qtl e geralmente precisa de alguma curadoria manual. A segunda maneira usa o algoritmo MDS e é mais rápido e geralmente mais preciso. Você pode escolher qual método deseja usar e pular para sua seção específica, o que significa que você não precisa executar para os dois lados.orderMarkers()

Usando orderMarkers a função por R/qtl

R/qtl tem uma função que executa o algoritmo Branch-and-Bound como uma possível solução para o Problema do Vendedor de Viagens (TSP) que é o marcador de pedidos. Geralmente fornece uma boa solução. O problema é que Branch-and-Bound é muito sensível à escolha do marcador que é feita para iniciar o algoritmo. Portanto, executamos pelo menos algumas vezes para que algum efeito das primeiras escolhas possa ser avaliado.

Nós salvamos o objeto sob dois novos objetos chamados e, assim, podemos atualizar com os resultados de duas corridas do algoritmo Branch-and-Bound. Além disso, inicializamos dois objetos que armazenarão a probabilidade de registro do pedido para cada grupo de linkage de ambas as corridas:population_Z006.bb1, population_Z006.bb2

population_Z006.bb1 <- population_Z006

population_Z006.bb2 <- population_Z006

loglik.bb1 <- loglik.bb2 <- c()
c <- 1
plotRF(population_Z006, chr=c, col.scheme = "redblue")

c <- 2
plotRF(population_Z006, chr=c, col.scheme = "redblue")

c <- 3
plotRF(population_Z006, chr=c, col.scheme = "redblue")

c <- 4
plotRF(population_Z006, chr=c, col.scheme = "redblue")

c <- 5
plotRF(population_Z006, chr=c, col.scheme = "redblue")

c <- 6
plotRF(population_Z006, chr=c, col.scheme = "redblue")

c <- 7
plotRF(population_Z006, chr=c, col.scheme = "redblue")

c <- 8
plotRF(population_Z006, chr=c, col.scheme = "redblue")

c <- 9
plotRF(population_Z006, chr=c, col.scheme = "redblue")

c <- 10
plotRF(population_Z006, chr=c, col.scheme = "redblue")

memory.limit(9999999999)
## [1] 1e+10

Grupo de ligação 1

O objeto armazena o número do cromossomo em avaliação.c

c <- 1
plotRF(population_Z006, chr=c, col.scheme = "redblue")

O argumento vamos dar uma olhada mais de perto no mapa de calor de um cromossomo específico, cujos marcadores claramente não são ordenados.chr

population_Z006.bb1 <- orderMarkers(cross = population_Z006.bb1, chr=c, use.ripple = FALSE, map.function="kosambi")
plotRF(population_Z006.bb1, chr = c, col.scheme = "redblue")

pull.map(population_Z006.bb1, chr = c)
##    PZA00276.18     PZB01227.6     PZA02044.1    PZA00307.14     PZA00991.2 
##   0.000000e+00   5.000001e-06   1.000000e-05   1.500000e-05   6.293577e+00 
##     PZA00235.9    PZA02359.10     PZA00623.3      PHM9807.9   PZA01238.1.2 
##   7.461849e+00   9.052711e+00   9.052716e+00   1.014222e+01   1.049885e+01 
##     PZA01068.1    PZA00343.31     PHM1275.22     PZA00856.2    PZA00243.25 
##   1.049885e+01   1.092123e+01   1.197655e+01   1.197724e+01   1.197724e+01 
##     PZA01239.2     PHM7616.35     PZA01807.1     PZA00432.4     PZA03613.1 
##   1.536560e+01   1.536561e+01   1.536561e+01   1.536562e+01   4.009893e+02 
##     PZA01271.1     PZA02129.1     PZA02032.1    PHM2244.142     PZA02372.1 
##   4.009893e+02   4.034567e+02   4.043264e+02   4.097052e+02   4.121396e+02 
##     PHM6238.36     PZA00175.2     PZA00528.1     PZA00447.8     PZA00181.2 
##   4.134812e+02   4.152862e+02   4.152862e+02   4.152862e+02   4.152862e+02 
##     PZA02284.1     PZA00731.7     PZA00566.5     PZA00887.1    PZA00106.10 
##   4.199949e+02   4.199949e+02   4.220410e+02   4.229607e+02   4.229607e+02 
##     PZA03521.1     PZA03551.1     PZA01497.1      csu1171.2     PZA02393.2 
##   4.229607e+02   4.250480e+02   4.311016e+02   4.311016e+02   4.347221e+02 
##     PZB00648.5     PZA01652.1     PZA02094.9     PZB00718.5     PZA01030.1 
##   4.347221e+02   4.347221e+02   4.347221e+02   4.362755e+02   4.381024e+02 
##    PZA00425.11     PHM13619.5     PHM3226.15     PHM4531.46     PZB01957.1 
##   4.455412e+02   4.455412e+02   4.487030e+02   4.487030e+02   4.487030e+02 
##     PZA02487.1     PZA01455.1     PZB02058.1     PZA01348.1     PZA02490.1 
##   4.487030e+02   4.532509e+02   4.532509e+02   4.532509e+02   4.532509e+02 
##     PZB01662.1     PZA02686.1     PZA02271.1     PZA02195.1    PHM3726.129 
##   4.532509e+02   4.532509e+02   4.575621e+02   4.578865e+02   4.608627e+02 
##     PZA00240.6     PZA02376.1     PZA00962.1     PZA03742.1     PZA03243.2 
##   4.608627e+02   4.612116e+02   4.612116e+02   4.625847e+02   4.625847e+02 
##    PZA00081.18        umc13.1     PHM4913.18     PZA03183.5     PZB00872.3 
##   4.635346e+02   4.641480e+02   4.644696e+02   4.644696e+02   4.644696e+02 
##     PZA02292.1     PZA03168.5     PZA02737.1     PZA02550.1     PZA02114.1 
##   4.662643e+02   4.662643e+02   4.700346e+02   4.704035e+02   4.707422e+02 
##     PZB01062.3     PZA03561.1     PZA01315.1     PZA01476.1    PZA00294.22 
##   4.707422e+02   4.707450e+02   4.717013e+02   4.720165e+02   4.730182e+02 
##     PZA03189.4     PZA01267.3     PHM5098.25     PZA00752.1     PZA01135.1 
##   4.733646e+02   4.796684e+02   4.796684e+02   4.817195e+02   4.832863e+02 
##   PZA03240.1.2   PZA00944.1.2     PZA03465.1     PZB01235.4     PZA02577.1 
##   4.862019e+02   4.865480e+02   4.865480e+02   4.887153e+02   4.917986e+02 
##     PZA03200.2    csu1138.3.4     PZA02070.1     PHM9418.11     PZA02763.1 
##   4.917986e+02   4.924350e+02   4.924350e+02   4.930801e+02   4.930801e+02 
##     PZA01254.2     PZA00939.1     PZA02750.3     PZA03037.2   PZA03305.7.1 
##   4.930801e+02   4.930801e+02   4.930801e+02   5.675905e+02   5.675905e+02 
##     PZA00894.7     PZB01403.1    PHM18705.23     PZA02087.2     PZA01246.1 
##   5.678855e+02   5.690750e+02   5.694148e+02   5.711603e+02   5.711603e+02 
##    PZA00245.20     PZA00978.1     PZA03020.8     PZA02957.5     PZA03188.3 
##   5.711603e+02   5.714463e+02   5.717231e+02   5.720144e+02   5.744005e+02 
##    PZA00610.16     PZA02204.1     PZB00114.1    PZA01978.23     PZA02520.1 
##   5.752629e+02   5.778977e+02   5.789900e+02   5.789900e+02   5.807452e+02 
##    PZA00030.11     PZA02698.3     PZB00895.1     PZA02278.1 PZA01921.20.19 
##   5.852413e+02   5.852414e+02   5.875087e+02   5.875087e+02   5.948660e+02 
##     PZA00339.4     PZA03457.1     PZA02985.5     PHM5526.25     PZB00008.1 
##   5.948660e+02   5.960090e+02   5.960090e+02   5.960090e+02   5.960090e+02 
##     PZB00063.1     PHM14475.7     PZA01588.1         glb1.2         kip1.3 
##   5.960090e+02   6.030956e+02   6.030956e+02   6.030956e+02   6.041978e+02 
##      PHM3034.3   PZA02269.3.4     PZA03404.1    PHM16605.19     PZA03064.6 
##   6.041978e+02   6.070207e+02   6.070207e+02   6.092271e+02   6.099434e+02 
##     PZA00381.4     PZA03301.2    PZA03001.15     PHM4926.16     PZA00664.3 
##   6.145909e+02   6.145909e+02   6.145909e+02   6.145909e+02   6.218464e+02 
##     PZA02186.1     PZB01647.1    PZA00658.21       umc128.2     PZA02823.1 
##   6.218464e+02   6.218464e+02   6.229414e+02   6.229414e+02   6.229414e+02 
##     PHM2478.22     PHM4942.12     PZA02117.1     PHM5484.22    PHM15871.11 
##   6.229414e+02   6.229414e+02   6.235334e+02   6.250205e+02   6.250205e+02 
##     PZA03741.1     PHM6043.19     PZA01039.1    PHM12706.14     PZA03265.3 
##   6.283075e+02   6.289283e+02   6.318643e+02   6.318643e+02   6.318643e+02 
##     PZA02014.3     PHM5480.17     PZA03193.2     PZA03194.1     PZA01019.1 
##   6.318643e+02   6.324572e+02   6.324572e+02   6.324572e+02   6.399156e+02 
##    PZA01963.15    PZA00131.15     PZA01391.1     PZA01216.1    PZA03074.27 
##   6.399157e+02   6.399157e+02   6.399157e+02   6.399157e+02   6.420129e+02 
##     PZA00619.3    PZA02467.10     PZA03531.1     PZA00068.1     PHM1968.22 
##   6.462185e+02   6.462185e+02   6.582310e+02   6.582310e+02   6.588563e+02 
## PZA00455.14.16     PZA02191.1     PZA02135.2     PZA02741.1          an1.5 
##   6.626041e+02   6.626041e+02   6.660536e+02   6.660536e+02   6.660536e+02
(loglik.bb1[c] <- attr(population_Z006.bb1$geno[[c]]$map, "loglik"))
## [1] -2839.988
population_Z006.bb2 <- orderMarkers(cross = population_Z006.bb2, chr=c, use.ripple = FALSE, map.function="kosambi")
plotRF(population_Z006.bb2, chr = c, col.scheme = "redblue")

pull.map(population_Z006.bb2, chr = c)
##     PZA02044.1    PZA00307.14     PZA00991.2    PZA00276.18     PZA00235.9 
##       0.000000       6.288824       6.288829       6.288834       7.457576 
##     PZA00623.3      PHM9807.9    PZA02359.10   PZA01238.1.2     PZA01068.1 
##       9.048323      10.137840      10.137845      10.494573      10.494578 
##    PZA00343.31     PZA00856.2     PHM1275.22    PZA00243.25     PZA01239.2 
##      10.916949      11.972992      11.972997      11.973002      15.361358 
##     PZA00432.4     PHM7616.35     PZA01807.1     PZA03613.1     PZA01271.1 
##      15.361363      15.361368      15.361373     400.985085     400.985090 
##     PZA02129.1     PZA02032.1    PHM2244.142     PZA02372.1     PHM6238.36 
##     403.452499     404.322147     409.700986     412.135340     413.476917 
##     PZA00528.1     PZA00181.2     PZA00175.2     PZA02284.1     PZA00447.8 
##     415.281921     415.281926     415.281931     419.990616     419.990621 
##     PZA00731.7     PZA00566.5     PZA00887.1    PZA00106.10     PZA03521.1 
##     419.990626     422.036740     422.956400     422.956405     422.956410 
##     PZA03551.1     PZA01497.1      csu1171.2     PZA01652.1     PZA02094.9 
##     425.043729     431.097527     431.097532     431.097537     431.097542 
##     PZB00648.5     PZA02393.2     PZB00718.5     PZA01030.1     PZA02487.1 
##     434.718503     434.718508     436.271561     438.098506     445.538502 
##     PHM4531.46     PHM13619.5    PZA00425.11     PZB02058.1     PHM3226.15 
##     445.538507     445.538512     445.538517     448.697781     448.697786 
##     PZB01957.1     PZA02686.1     PZA01348.1     PZB01662.1     PZA01455.1 
##     448.697791     453.233400     453.233405     453.233410     453.233415 
##     PZA02490.1     PZA02271.1     PZA02195.1    PHM3726.129     PZA00240.6 
##     453.233420     457.497182     457.818630     460.724749     460.724754 
##     PZA00962.1     PZA02376.1    PZA00081.18     PZA03742.1     PZA03243.2 
##     461.066486     461.066491     463.358503     464.163812     464.163817 
##        umc13.1     PHM4913.18     PZA03183.5     PZB00872.3     PZA02292.1 
##     465.719033     466.038898     466.038903     466.038908     467.839575 
##     PZA03168.5     PZA02737.1     PZA02550.1     PZA02114.1     PZA03561.1 
##     467.839580     471.507929     471.868818     472.200044     472.200049 
##     PZB01062.3     PZA01315.1     PZA01476.1     PZA03189.4    PZA00294.22 
##     472.200054     473.086287     473.378085     474.291576     474.307165 
##     PZA03064.6    PHM16605.19   PZA02269.3.4     PZA03404.1      PHM3034.3 
##     517.872586     518.466266     520.333343     520.333348     522.857043 
##         kip1.3     PZA01588.1         glb1.2     PHM14475.7     PZA00339.4 
##     522.857048     523.922441     523.922446     523.922451     529.407623 
## PZA01921.20.19     PZA03457.1     PZA02985.5     PZB00008.1     PHM5526.25 
##     529.407628     530.555552     530.555557     530.555562     530.555567 
##     PZB00895.1     PZB00063.1     PZA02278.1     PZA02698.3    PZA00030.11 
##     537.078788     537.078793     537.078798     539.364352     539.364357 
##     PZA02520.1     PZB00114.1    PZA01978.23     PZA02204.1    PZA00610.16 
##     543.860397     545.607606     545.607611     546.692017     549.310856 
##     PZA03188.3     PZA02957.5     PZA03020.8    PHM18705.23    PZA00245.20 
##     550.168619     552.535981     552.535986     554.424959     554.424964 
##   PZA03305.7.1     PZB01227.6     PZA03037.2     PZA00894.7     PZB01403.1 
##     556.645712     556.645717     556.929700     557.213387     558.367382 
##     PZA01246.1     PZA02087.2     PZA00978.1     PHM4926.16     PZB01647.1 
##     560.422122     560.422127     560.701663     599.531169     599.531174 
##     PZA00381.4     PZA03301.2    PZA03001.15     PZA02186.1       umc128.2 
##     599.531179     599.531184     605.264311     605.264316     606.346351 
##     PHM2478.22     PZA02823.1     PHM4942.12     PZA00664.3    PZA00658.21 
##     606.346356     606.346361     606.346366     606.346371     606.346376 
##     PZA02117.1     PHM5484.22    PHM15871.11     PZA03741.1     PZA03265.3 
##     606.934838     608.413868     608.413873     611.705493     612.327397 
##     PHM6043.19     PZA02014.3     PZA01039.1     PZA03193.2    PHM12706.14 
##     612.327402     615.267161     615.267166     615.859923     615.859928 
##    PZA00131.15     PHM5480.17     PZA01391.1     PZA03194.1    PZA01963.15 
##     623.363858     623.363863     623.363868     623.363873     623.363878 
##     PZA01019.1     PZA01216.1    PZA03074.27     PZA00619.3    PZA02467.10 
##     623.363883     623.363888     625.467525     629.676025     629.676030 
##     PZA03531.1     PZA00068.1     PHM1968.22 PZA00455.14.16     PZA02191.1 
##     641.787032     641.787037     642.421202     646.373886     646.373891 
##          an1.5     PZA02135.2     PZA03200.2     PZA02741.1     PZA02577.1 
##     650.249523     655.662626     655.662631     655.662636     656.008618 
##    csu1138.3.4     PZA02070.1     PZA01254.2     PHM9418.11     PZA02763.1 
##     656.012638     656.705913     657.457929     657.457934     657.457939 
##     PZA02750.3     PZA00939.1     PZB01235.4   PZA00944.1.2     PZA03465.1 
##     657.457944     657.457949     659.649212     661.727835     661.727840 
##   PZA03240.1.2     PZA01135.1     PZA00752.1     PHM5098.25     PZA01267.3 
##     662.060832     664.841344     666.328429     668.149776     668.149781
(loglik.bb2[c] <- attr(population_Z006.bb2$geno[[c]]$map, "loglik"))
## [1] -2934.306
save.image("population_Z006.RData")

Grupo de ligação 2

c <- 2
plotRF(population_Z006, chr=c, col.scheme = "redblue")

population_Z006.bb1 <- orderMarkers(cross = population_Z006.bb1, chr=c, use.ripple = FALSE, map.function="kosambi")
plotRF(population_Z006.bb1, chr = c, col.scheme = "redblue")

pull.map(population_Z006.bb1, chr = c)
##     PZA01887.1     PZA02367.1     PZA00191.5     PZA00818.1     PZA01983.1 
##      0.0000000      0.2779921      0.5660755      0.5660805      0.8236731 
##     PZA01438.1     PHM5359.10    PZA02316.22     PZA01570.1    PHM13122.43 
##      9.1412540      9.1422131      9.1422444     11.8491798     16.6241608 
##    PZA02653.12     PZA02462.1     PZA02753.1     PZB00054.3     PHM3137.17 
##     24.1729660     27.8546096     28.2886521     28.2886571     31.5549217 
##     PZA01371.1     PZB00094.1    PZA02029.21     PZA01925.1     PZA00865.1 
##     34.6890596     34.6890646     34.6890696     34.6890746     35.0522466 
##     PZB00079.4     PZA01284.6     PZA03092.7     PZA00112.5     PZA00985.1 
##     41.7941541     43.4329278     43.4329328     45.3286365     46.4896298 
##     PZA01327.1     PZA01523.1     PZA03578.1     PZA03226.3     PZA03274.4 
##     49.5321777     51.4228663     51.4228713     53.2218509     53.2218559 
##     PZA00517.7     PZA01427.1 PZA02792.26.25     PZA02113.1      PHM4647.8 
##     53.2218609     55.1219620     55.1219670     59.8232999     59.8233049 
##      PHM565.31     PHM12992.5     PZA00934.2     PZA01530.1     PZA00499.3 
##     59.8233099     59.8233149     59.8233199     59.8233249     59.8233299 
##     PHM1870.20     PZA00801.1     PZB00869.4     PZA01804.1     PHM16854.3 
##     67.4766000     67.4766050     67.4766100     67.4766150     67.4766200 
##     PZA00805.1     PZA00222.7     PZA03451.5     PZA00981.3  PZA00522.12.7 
##     67.4766250     67.4766300     67.4766350     67.4766400     67.4766450 
##     PZA02207.1     PZA01563.1     PZA00996.1     PHM3691.18     PZB01115.3 
##     67.4766500     67.4766550     67.4766600     67.4766650     68.5212602 
##     PZA02676.2      PHM3171.5     PHM4165.14     PZB00232.2     PZA02525.1 
##     68.5212652     69.1848733     69.5185385     69.5185435     70.7085083 
##     PZA03677.1     PZA01050.1     PHM5798.39     PZB01112.1     PZA01349.2 
##     70.7085133     70.7085183     70.7085233     70.7085283     70.7085333 
##     PZA00261.6     PZA01303.1     PZA02862.3     PZA02818.6     PZA01779.1 
##     71.6210732     71.6210782     73.2327834     73.4100799     73.5901497 
##     PZA00273.5     PZA01693.1    PZA03049.24    PZA00643.13     PZA00881.1 
##     75.2314812     75.2314862     76.5525257     77.8678294     77.8678344 
##    PZA02164.16     PZA01365.1     PZB01017.1    PZA00067.10     PZA01608.1 
##     77.8678394     80.7937851     81.8624047     81.8624097     83.9485857 
##     PZA01796.1     PZA00148.3     PZA02981.2     PZA01763.2        ae1.8.7 
##     84.5930243     86.3636287     88.8937108     88.8937158     88.8937208 
##     PZA03536.1     PZA02641.2   PZA01294.2.1    PZA00255.14     PZA00987.1 
##     89.5254044     89.5254094     89.5254144     89.5254194     90.9142109 
##     PZA02040.2    PZA00300.14     PZA01410.1     PZA03717.1     PZA03714.1 
##     90.9142159     90.9142209     91.2907777     94.6395126     94.6395176 
##     PZA02633.4      PHM5296.6     PZA02356.7     PZA02411.3     PZA02751.1 
##     95.4326478     95.4326528     95.4326578     95.4326628     95.4326678 
##     PZA03452.6     PZA03324.1     PZA03317.1     PZA02408.2      PHM532.23 
##     95.4326728     95.4326778     95.4326828     95.4326878     95.4326928 
##     PZA03320.6    PHM1899.157     PZA03172.3     PZA01304.1     PZA02209.2 
##     95.4326978     95.4327028     95.4327078     95.4327128     95.4327178 
##     PZA02426.1    PZA00352.23     PZA01575.1     PZA02383.1    PZA00652.17 
##     95.4327228     95.4327278     95.4327328     95.4327378     99.1277731 
##    PZA03024.16     PZA01142.4     PZA01265.1    PZA02820.17     PZA00395.2 
##     99.4709981     99.8151822    101.5064565    101.5064615    101.8144177 
##     PZA02060.1     PZA02667.1     PZA02513.1     PZB00765.1    PHM3512.186 
##    101.8144227    101.8144277    101.8144327    101.8144377    103.0302873 
##     PZA00963.3     PZA00980.1     PZA03167.5     PZA02068.1    PZA02015.11 
##    105.4007468    105.4007518    119.2762911    119.2762961    119.2763011 
##     PZA01680.3    PZA00545.26     PZA00836.1     PZA01259.1     PZA01140.1 
##    119.2763061    119.2763111    119.2763161    119.2763211    134.5669871 
##     PZA02480.1     PZA01060.1     PZA02390.1     PZA02769.1 
##    150.3227178    150.3227228    150.3227278    150.3227328
(loglik.bb1[c] <- attr(population_Z006.bb1$geno[[c]]$map, "loglik"))
## [1] -1670.498
population_Z006.bb2 <- orderMarkers(cross = population_Z006.bb2, chr=c, use.ripple = FALSE, map.function="kosambi")
plotRF(population_Z006.bb2, chr = c, col.scheme = "redblue")

pull.map(population_Z006.bb2, chr = c)
##     PZA00818.1     PZA01887.1     PZA01983.1     PZA02367.1     PZA00191.5 
##      0.0000000      0.2743339      0.2743389      0.2743439      8.6622664 
##    PZA02316.22     PZA01438.1     PZA01570.1     PHM5359.10    PHM13122.43 
##      8.6622714      8.6622764     11.3762952     11.3763002     16.1537513 
##    PZA02653.12     PZA02462.1     PZB00054.3     PHM3137.17     PZA02753.1 
##     23.7017531     27.3831429     27.8168702     31.0831248     31.0831298 
##     PZB00094.1     PZA01371.1    PZA02029.21     PZA01925.1     PZA00865.1 
##     34.2176073     34.2176123     34.2176173     34.2176223     34.5807953 
##     PZB00079.4     PZA01284.6     PZA03092.7     PZA00112.5     PZA00985.1 
##     41.3227180     42.9614931     42.9614981     44.8572015     46.0181948 
##     PZA03578.1     PZA01327.1     PZA01523.1     PZA03274.4     PZA00517.7 
##     49.0609315     49.0609365     50.9516250     52.7504102     52.7504152 
##     PZA03226.3 PZA02792.26.25     PZA01563.1     PZA00981.3      PHM565.31 
##     52.7504202     54.6501375     59.3519594     59.3519644     59.3519694 
##     PZA00934.2     PZA00499.3     PHM3691.18     PZA02113.1     PZA01427.1 
##     59.3519744     59.3519794     59.3519844     59.3519894     59.3519944 
##     PZA00801.1     PZB00869.4     PZA00805.1     PHM12992.5     PZA01530.1 
##     67.0052398     67.0052448     67.0052498     67.0052548     67.0052598 
##     PZA02207.1      PHM4647.8     PZA03451.5  PZA00522.12.7     PHM16854.3 
##     67.0052648     67.0052698     67.0052748     67.0052798     67.0052848 
##     PZA00996.1     PZA01804.1     PZA00222.7     PZA02676.2     PHM1870.20 
##     67.0052898     67.0052948     67.0052998     68.0497129     68.0497179 
##      PHM3171.5     PZB01115.3     PZB00232.2     PZA01349.2     PZA02525.1 
##     68.7134291     68.7134341     69.0470459     70.2371498     70.2371548 
##     PZB01112.1     PZA03677.1     PZA01050.1     PHM4165.14     PZA01303.1 
##     70.2371598     70.2371648     70.2371698     70.2371748     71.1493698 
##     PHM5798.39     PZA02862.3     PZA00261.6     PZA01779.1     PZA02818.6 
##     71.1493748     72.7610092     72.7610142     73.1200787     73.1200837 
##     PZA00273.5     PZA01693.1    PZA03049.24    PZA00643.13    PZA02164.16 
##     74.7614041     74.7614091     76.0824257     77.3977220     77.3977270 
##     PZA00881.1     PZA01365.1    PZA00067.10     PZB01017.1     PZA01608.1 
##     77.3977320     80.3236686     81.3920803     83.4768105     83.4781246 
##     PZA01796.1     PZA00148.3     PZA02981.2     PZA01763.2        ae1.8.7 
##     84.1227705     85.8933716     88.4234488     88.4234538     88.4234588 
##   PZA01294.2.1     PZA03536.1     PZA02641.2    PZA00255.14     PZA02040.2 
##     89.0551423     89.0551473     89.0551523     89.0551573     90.4439470 
##    PZA00300.14     PZA00987.1     PZA01410.1     PZA03714.1     PZA03717.1 
##     90.4439520     90.4439570     90.8205082     94.1692381     94.1692431 
##      PHM5296.6    PZA00352.23     PZA03317.1     PZA02426.1     PZA02383.1 
##     94.9623718     94.9623768     94.9623818     94.9623868     94.9623918 
##     PZA02356.7     PZA03172.3     PZA03320.6     PZA01304.1     PZA02751.1 
##     94.9623968     94.9624018     94.9624068     94.9624118     94.9624168 
##     PZA02411.3     PZA03324.1     PZA02408.2     PZA01575.1      PHM532.23 
##     94.9624218     94.9624268     94.9624318     94.9624368     94.9624418 
##    PHM1899.157     PZA03452.6     PZA02209.2     PZA02633.4    PZA00652.17 
##     94.9624468     94.9624518     94.9624568     94.9624618     98.6574903 
##    PZA03024.16     PZA01142.4     PZA01265.1    PZA02820.17     PZA02060.1 
##     99.0007148     99.3448985    101.0361668    101.0361718    101.3441271 
##     PZB00765.1     PZA02667.1     PZA02513.1     PZA00395.2    PHM3512.186 
##    101.3441321    101.3441371    101.3441421    101.3448412    102.5599524 
##     PZA00963.3    PZA00545.26     PZA00836.1     PZA01259.1     PZA01680.3 
##    104.9294220    118.8087989    118.8088039    118.8088089    118.8088139 
##     PZA02068.1     PZA00980.1    PZA02015.11     PZA03167.5     PZA01140.1 
##    118.8088189    118.8088239    134.0949728    134.0949778    134.0949828 
##     PZA01060.1     PZA02390.1     PZA02480.1     PZA02769.1 
##    149.8522588    149.8522638    149.8522688    149.8522738
(loglik.bb2[c] <- attr(population_Z006.bb2$geno[[c]]$map, "loglik"))
## [1] -1667.511
save.image("population_Z006.RData")

Grupo de ligação 3

c <- 3
plotRF(population_Z006, chr=c, col.scheme = "redblue")

population_Z006.bb1 <- orderMarkers(cross = population_Z006.bb1, chr=c, use.ripple = FALSE, map.function="kosambi")
plotRF(population_Z006.bb1, chr = c, col.scheme = "redblue")

pull.map(population_Z006.bb1, chr = c)
##     PZA00088.3     PZA02423.1     PHM2423.33     PZA02182.1     PHM3852.23 
##   0.000000e+00   5.000001e-06   2.390701e+00   1.038442e+01   1.138521e+01 
##     PZA01688.3     PZA01360.3    PZA00316.10     PZA00402.1     PZA00219.7 
##   1.138522e+01   1.138522e+01   1.235097e+01   1.887097e+01   2.287366e+01 
##     PHM2672.19     PZA03391.1     PZA02668.2     PZA02514.1     PZA02824.4 
##   2.287367e+01   2.287367e+01   2.471800e+01   2.593521e+01   2.593522e+01 
##     PZA01154.1     PZA01233.1         sh2.21     PZA00750.1     PHM3342.31 
##   2.593522e+01   2.593523e+01   3.050695e+01   3.050695e+01   3.050696e+01 
##     PZA02665.2     PZA03146.4     PZA02616.1     PZB01457.1     PZA02516.1 
##   3.050696e+01   3.114506e+01   3.808368e+01   3.808368e+01   4.008746e+01 
## PZA00538.18.15     PZA02733.1     PZA03154.4     PZA00892.5     PZA01501.1 
##   4.388314e+01   4.994504e+01   4.994504e+01   5.242760e+01   5.430068e+01 
##     PZA02122.9    PZA00308.24     PZA01035.1     PZB01109.1     PZA01457.1 
##   5.463352e+01   5.733122e+01   5.733122e+01   5.733123e+01   5.733123e+01 
##     PZA03255.1     PZA01228.2    PHM13673.53      PHM824.17     PZA00494.2 
##   5.934354e+01   6.062248e+01   6.062248e+01   6.062249e+01   6.062249e+01 
##     PZA03647.1     PZA03744.1     PZA03743.1   PZA03191.1.4         zb27.1 
##   6.062250e+01   6.062250e+01   6.062251e+01   6.270783e+01   6.270783e+01 
##     PZA03733.1     PZA03735.1     PHM1675.29    PZA01962.12     PZA02654.3 
##   6.270784e+01   6.270784e+01   7.019127e+01   7.458216e+01   7.458216e+01 
##     PHM17210.5     PZA02212.1     PZA01726.1     PZA00783.1    PZA03032.19 
##   7.458217e+01   8.128863e+01   8.187174e+01   8.379991e+01   8.410841e+01 
##     PZD00027.2     PHM1959.26     PZA02402.1 PZA03073.28.26     PZA00186.4 
##   8.514585e+01   8.514586e+01   8.514586e+01   8.629429e+01   8.744412e+01 
##     PHM2885.31     PZA01396.1     PHM4621.57     PHM9914.11     PZA00667.2 
##   8.744412e+01   8.858949e+01   8.858949e+01   8.981643e+01   8.981644e+01 
##     PZB02179.1     PZA00828.2     PZA00948.1     PZA00827.1     PZA01934.6 
##   9.329339e+01   9.329339e+01   9.384030e+01   9.458220e+01   9.532615e+01 
##     PHM4955.12     PZB02044.1      PHM890.20     PZB02122.1     PZA02474.1 
##   9.532615e+01   9.597938e+01   9.597939e+01   9.597939e+01   9.640493e+01 
##     PZA00920.1     PZD00015.5     PZD00016.4     PZA00363.7     PHM1745.16 
##   9.640494e+01   9.897006e+01   9.897006e+01   9.897007e+01   9.897007e+01 
##     PZB02002.1    PZA02299.16 PZA00413.20.18    PHM15449.10     PZA03198.3 
##   9.897008e+01   1.004457e+02   1.011380e+02   1.011380e+02   1.019054e+02 
##     PZA02619.1     PZA02134.3     PZA02742.1     PHM5502.31     PZA00707.9 
##   1.036500e+02   1.045814e+02   1.045814e+02   1.045814e+02   1.045814e+02 
##     PZA02699.1     PZA02645.2     PZA02296.1     PZA02589.1     PZA00265.6 
##   1.045814e+02   1.045814e+02   1.057533e+02   1.057556e+02   1.057556e+02 
##     PZA00509.1     PZA01447.1     PZA00279.2     PHM15474.5     PZA03119.1 
##   1.057556e+02   1.062046e+02   1.062046e+02   1.062046e+02   1.062046e+02 
##         zb21.1     PZA01114.2     PHM15899.9    PZA00380.10    PZA00348.11 
##   1.071155e+02   1.071155e+02   1.071155e+02   1.071155e+02   1.071156e+02 
##     PZA00297.2     PZA03070.9     PHM13823.7     PZA00581.3     PHM2343.25 
##   1.071156e+02   1.071156e+02   1.071156e+02   1.071156e+02   1.074150e+02 
##     PZA02255.2     PZA03054.5     PZA01473.1     PZA02427.1   PZA00210.1.9 
##   1.074150e+02   1.074150e+02   1.074150e+02   1.074150e+02   1.074150e+02 
##     PHM4145.18     PHM4204.69     PZA01765.1     PZA00508.2     PZA02098.2 
##   1.113045e+02   1.198512e+02   1.269967e+02   1.269967e+02   1.315468e+02 
##     PZB01944.1     PZA00749.1     PZA03212.3     PHM12859.7     PZA03527.1 
##   1.315468e+02   1.347438e+02   1.350852e+02   1.383729e+02   1.383729e+02 
##    PZA00100.10     PZA02678.1     PZA02090.1     PZD00038.2     PZA00309.1 
##   1.402500e+02   1.405749e+02   1.486642e+02   1.486642e+02   1.578291e+02
(loglik.bb1[c] <- attr(population_Z006.bb1$geno[[c]]$map, "loglik"))
## [1] -1926.808
population_Z006.bb2 <- orderMarkers(cross = population_Z006.bb2, chr=c, use.ripple = FALSE, map.function="kosambi")
plotRF(population_Z006.bb2, chr = c, col.scheme = "redblue")

pull.map(population_Z006.bb2, chr = c)
##     PZA00088.3     PZA00309.1     PZA02090.1     PZA02678.1     PZD00038.2 
##         0.0000       385.6237       394.7879       402.8771       402.8771 
##    PZA00100.10     PZA03527.1     PHM12859.7     PZA03212.3     PZA00749.1 
##       403.2028       405.0799       405.0799       408.3675       408.7090 
##     PZB01944.1     PZA02098.2     PZA00508.2     PZA01765.1     PHM4204.69 
##       411.9063       411.9063       416.4543       416.4543       423.5957 
##     PHM4145.18     PZA02427.1     PHM2343.25     PHM13823.7     PZA01114.2 
##       432.1496       433.9901       435.8303       436.1620       436.1620 
##     PZA03070.9     PZA00297.2    PZA00380.10     PZA01473.1     PZA02255.2 
##       436.1620       436.1620       436.1620       436.4842       436.4842 
##   PZA00210.1.9         zb21.1     PZA03054.5    PZA00348.11     PHM15899.9 
##       436.4842       436.4842       436.4842       436.4842       437.1201 
##     PZA00279.2     PHM15474.5     PZA01447.1     PZA00581.3     PZA03119.1 
##       437.7559       437.7559       437.7560       437.7560       437.7560 
##     PZA00265.6     PZA00509.1     PZA02589.1     PZA02699.1     PZA02645.2 
##       438.1773       438.1773       438.1773       438.7127       439.2519 
##     PZA02742.1     PZA02296.1     PZA00707.9     PHM5502.31     PZA02134.3 
##       439.2519       439.2519       439.2519       439.2519       439.2519 
##     PZA02619.1 PZA00413.20.18     PZA03198.3    PHM15449.10    PZA02299.16 
##       440.1568       442.7276       442.7276       442.7276       443.4237 
##     PHM1745.16     PZD00015.5     PZD00016.4     PZB02002.1     PZA00363.7 
##       444.9102       444.9102       444.9102       444.9102       444.9102 
##     PZA00920.1     PZA02474.1     PZB02044.1      PHM890.20     PZB02122.1 
##       447.4955       447.4955       447.9248       447.9248       447.9248 
##     PHM4955.12     PZA01934.6     PZA00827.1     PZA00948.1     PZA00828.2 
##       448.5771       448.5771       449.3231       450.0670       450.6151 
##     PZB02179.1     PHM9914.11     PZA00667.2     PZA01396.1     PHM4621.57 
##       450.6151       452.2825       453.9539       455.1905       455.7583 
##     PHM2885.31     PZA00186.4     PHM1959.26 PZA03073.28.26     PZD00027.2 
##       456.3248       456.3248       457.4781       457.4781       458.6272 
##    PZA03032.19     PZA02402.1     PZA00783.1     PZA01726.1     PZA02212.1 
##       459.6645       459.6645       459.9731       461.9015       462.4847 
##     PZA02654.3     PHM17210.5    PZA01962.12     PZA03733.1     PZA03735.1 
##       469.1920       469.1920       469.1920       473.5837       473.5837 
##     PHM1675.29         zb27.1   PZA03191.1.4     PZA03744.1     PZA00494.2 
##       473.5837       481.0650       481.0650       483.1496       483.1496 
##     PZA03647.1     PZA01228.2      PHM824.17    PHM13673.53     PZA03743.1 
##       483.1496       483.1496       483.1497       483.1497       483.1497 
##     PZA03255.1    PZA00308.24     PZA01457.1     PZB01109.1     PZA01035.1 
##       484.4278       486.4389       486.4389       486.4389       486.4389 
##     PZA02122.9     PZA01501.1     PZA00892.5     PZA03154.4 PZA00538.18.15 
##       489.1350       489.4676       491.3391       493.8190       499.8774 
##     PZA02733.1     PZA02516.1     PZB01457.1     PZA02616.1     PHM3342.31 
##       499.8774       503.6581       505.6357       505.6358       513.4295 
##     PZA02665.2     PZA01154.1         sh2.21     PZA03146.4     PZA00750.1 
##       513.4295       513.4295       513.4295       513.4295       513.6918 
##     PZA02824.4     PZA02514.1     PZA01233.1     PZA02668.2     PHM2672.19 
##       518.5797       518.5797       518.5797       519.7940       521.6335 
##     PZA00219.7     PZA03391.1     PZA00402.1    PZA00316.10     PZA01360.3 
##       521.6335       521.6335       525.6290       532.1458       533.1112 
##     PZA01688.3     PHM3852.23     PZA02182.1     PHM2423.33     PZA02423.1 
##       533.1112       533.1112       534.1117       542.1033       544.4887
(loglik.bb2[c] <- attr(population_Z006.bb2$geno[[c]]$map, "loglik"))
## [1] -2089.495
save.image("population_Z006.RData")

Grupo de ligação 4

c <- 4
plotRF(population_Z006, chr=c, col.scheme = "redblue")

population_Z006.bb1 <- orderMarkers(cross = population_Z006.bb1, chr=c, use.ripple = FALSE, map.function="kosambi")
plotRF(population_Z006.bb1, chr = c, col.scheme = "redblue")

pull.map(population_Z006.bb1, chr = c)
##   PZA00680.3   PHM5817.15   PZA00365.2   PHM1511.14  PZA02133.10  PZA00525.17 
## 0.000000e+00 5.000001e-06 1.000000e-05 4.151674e+00 4.151679e+00 4.151684e+00 
##  PHM13440.13   PZA02681.8   PZA02175.1   PZA00902.1   PZA02264.5   PZB01233.1 
## 4.151689e+00 5.043615e+00 5.043620e+00 5.514068e+00 5.984278e+00 8.972924e+00 
##   PZA01211.1  PZA00613.22  PZA00172.12   PZA02208.1  PZA01935.10   PZA02081.1 
## 1.127069e+01 1.456996e+01 1.456996e+01 2.006717e+01 2.266478e+01 2.266479e+01 
##   PZA03747.1   PZA03699.1 PZB00901.3.4   PZA02272.3   PHM5822.15       zfl2.9 
## 2.466797e+01 2.466797e+01 3.135588e+01 3.212364e+01 3.368099e+01 4.045350e+01 
##   PZA01753.1   PZA02417.2   PZA02337.4   PZA03559.1   PZA00108.4   PZA02774.1 
## 4.433781e+01 4.477255e+01 4.477255e+01 4.477256e+01 4.524003e+01 7.625137e+01 
##   PZA02168.1   PZA03629.1   PZA02279.1    PHM3457.6   PZA00635.7  PHM10321.11 
## 7.787194e+01 7.787195e+01 8.142744e+01 8.142744e+01 8.142745e+01 8.257966e+01 
##  PHM13360.13   PZA02549.3  PHM4880.179   PZA01902.1   PZA02626.1   PZA00485.2 
## 8.373187e+01 8.373187e+01 8.373188e+01 8.373188e+01 8.698493e+01 8.698493e+01 
##    PHM3626.3  PZA00029.17   PZA01280.2   PZA03211.6   PZA01537.2  PZA02939.10 
## 8.698494e+01 8.698494e+01 8.761734e+01 8.761734e+01 8.823490e+01 8.854758e+01 
##   PZA01232.1   PZA01321.1   PZA02465.1   PZA00637.6  PZA00515.10   PZA02371.6 
## 8.854759e+01 8.991462e+01 8.991463e+01 9.092627e+01 9.092627e+01 9.092628e+01 
##   PZA03659.1     vdac1a.1   PZA00224.4   PZA03692.1   PZA00495.5   PZA03644.1 
## 9.313204e+01 9.313205e+01 9.313205e+01 9.313206e+01 9.313206e+01 9.313207e+01 
##   PZA01638.1   PZA03184.2   PZA00755.2   PZA03529.1   PZA01735.1    PHM3055.9 
## 9.313207e+01 9.774366e+01 1.054470e+02 1.054471e+02 1.054471e+02 1.110744e+02 
##   PZA00803.3   PZA02017.1   PZA02890.4   PZA00824.2   PHM7953.11   PHM3668.12 
## 1.110744e+02 1.110744e+02 1.110744e+02 1.110744e+02 1.110745e+02 1.110745e+02 
##  PHM16125.47   PZA02731.1   PZA03165.1   PZB01103.2   PZA01885.2   PZA02077.1 
## 1.113695e+02 1.113695e+02 1.124189e+02 1.134731e+02 1.145225e+02 1.145225e+02 
##   PZA00390.7   PZA02329.2   PZA02964.7   PHM14412.4   PZB00772.7   PZA03602.1 
## 1.145225e+02 1.145225e+02 1.145225e+02 1.145225e+02 1.193608e+02 1.193614e+02 
##   PZA02456.1   PZA00804.1   PZA02680.1   PZA02471.5   PZA02418.2   PZA02453.1 
## 1.204904e+02 1.216559e+02 1.225299e+02 1.233878e+02 1.233878e+02 1.321860e+02 
##   PZA02012.7  PZA00527.10   PZA01991.3   PZA00163.4   PZA02564.2   PZB01013.1 
## 1.321860e+02 1.321860e+02 1.351605e+02 1.351605e+02 1.354501e+02 1.363782e+02 
##   PZA02266.3   PZA01895.1   PZA01352.5   PZA02727.1   PHM3094.23   PZA02170.1 
## 1.392109e+02 1.398833e+02 1.412780e+02 1.416253e+02 1.544968e+02 1.544968e+02 
##   PZD00022.5   PZA03577.1   PZA03321.4   PZA02450.1   PZA01820.1   PZB00183.4 
## 1.580221e+02 1.590308e+02 1.603725e+02 2.416310e+02 2.416310e+02 2.432605e+02 
##   PZA02378.7   PZA01993.7   PHM10404.8   PZA02496.1   PZA02058.1   PHM4586.12 
## 2.459741e+02 2.459741e+02 2.459741e+02 2.459741e+02 2.470248e+02 2.478473e+02 
##   PZA01336.1   PZA01374.1   PZA02080.1   PHM1962.33   PZA01755.1   PZA03568.1 
## 2.478473e+02 2.492234e+02 2.507083e+02 2.507083e+02 2.507083e+02 2.507083e+02 
##   PZA03142.5   PZA01879.1   PZA00590.1    PHM6111.5   PZA03634.1   PZA03228.4 
## 2.537669e+02 2.555513e+02 2.558964e+02 2.565894e+02 2.572884e+02 2.582429e+02 
##   PZA00497.4 
## 2.591320e+02
(loglik.bb1[c] <- attr(population_Z006.bb1$geno[[c]]$map, "loglik"))
## [1] -2125.426
population_Z006.bb2 <- orderMarkers(cross = population_Z006.bb2, chr=c, use.ripple = FALSE, map.function="kosambi")
plotRF(population_Z006.bb2, chr = c, col.scheme = "redblue")

pull.map(population_Z006.bb2, chr = c)
##   PZA00680.3   PHM5817.15   PZA00365.2  PZA00525.17   PHM1511.14  PZA02133.10 
## 0.000000e+00 5.000001e-06 1.000000e-05 4.151660e+00 4.151665e+00 4.151670e+00 
##  PHM13440.13   PZA02681.8   PZA02175.1   PZA00902.1   PZA02264.5   PZB01233.1 
## 4.151675e+00 5.043598e+00 5.043603e+00 5.514049e+00 5.984258e+00 8.972889e+00 
##   PZA01211.1  PZA00172.12  PZA00613.22   PZA02208.1  PZA01935.10   PZA02081.1 
## 1.127071e+01 1.457009e+01 1.457009e+01 2.006624e+01 2.266346e+01 2.266347e+01 
##   PZA03747.1   PZA03699.1 PZB00901.3.4   PZA02272.3   PHM5822.15       zfl2.9 
## 2.466640e+01 2.466641e+01 3.134850e+01 3.211542e+01 3.367123e+01 4.039856e+01 
##   PZA03559.1   PZA02337.4   PZA02417.2   PZA01753.1   PZA00108.4   PZA02774.1 
## 4.500433e+01 4.500433e+01 4.500434e+01 4.557281e+01 4.557281e+01 7.657656e+01 
##   PZA02168.1   PZA03629.1   PZA00635.7   PZA02279.1  PHM4880.179   PZA01902.1 
## 7.819744e+01 7.819745e+01 8.175087e+01 8.175088e+01 8.411323e+01 8.411323e+01 
##  PHM13360.13  PHM10321.11   PZA02549.3    PHM3626.3    PHM3457.6  PZA00029.17 
## 8.411324e+01 8.411324e+01 8.411325e+01 8.411325e+01 8.411326e+01 8.736412e+01 
##   PZA03211.6   PZA00485.2   PZA02626.1   PZA01280.2   PZA01537.2   PZA01232.1 
## 8.736412e+01 8.736413e+01 8.736413e+01 8.799661e+01 8.861409e+01 8.892677e+01 
##  PZA02939.10   PZA01321.1   PZA02465.1   PZA02371.6   PZA03659.1   PZA00495.5 
## 8.892677e+01 9.029377e+01 9.029377e+01 9.130516e+01 9.351090e+01 9.351090e+01 
##     vdac1a.1   PZA01638.1   PZA03692.1   PZA03644.1   PZA00224.4   PZA00637.6 
## 9.351091e+01 9.351091e+01 9.351092e+01 9.351092e+01 9.351093e+01 9.351093e+01 
##  PZA00515.10   PZA03184.2   PZA03529.1   PZA01735.1   PZA00755.2   PZA02017.1 
## 9.351094e+01 9.812187e+01 1.058251e+02 1.058251e+02 1.058251e+02 1.114475e+02 
##   PZA02890.4   PHM7953.11   PZA00803.3   PZA00824.2   PHM3668.12    PHM3055.9 
## 1.114475e+02 1.114475e+02 1.114475e+02 1.114475e+02 1.114475e+02 1.114475e+02 
##   PHM14412.4   PZB01103.2   PZA02731.1  PHM16125.47   PZA03165.1   PZB00772.7 
## 1.117424e+02 1.117424e+02 1.117424e+02 1.117424e+02 1.117424e+02 1.150440e+02 
##   PZA00390.7   PZA02964.7   PZA01885.2   PZA02329.2   PZA02077.1   PZA03602.1 
## 1.150440e+02 1.150441e+02 1.150441e+02 1.150441e+02 1.150441e+02 1.198783e+02 
##   PZA02456.1   PZA00804.1   PZA02680.1  PZA00527.10   PZA02418.2   PZA02471.5 
## 1.210067e+02 1.221720e+02 1.230458e+02 1.239036e+02 1.239036e+02 1.239036e+02 
##   PZA02012.7   PZA02453.1   PZA00163.4   PZA01991.3   PZA02564.2   PZB01013.1 
## 1.327015e+02 1.356742e+02 1.356751e+02 1.358195e+02 1.359640e+02 1.368922e+02 
##   PZA02266.3   PZA01895.1   PZA01352.5   PZA02727.1   PZA02170.1   PZD00022.5 
## 1.397249e+02 1.403974e+02 1.417920e+02 1.421394e+02 1.550059e+02 1.585279e+02 
##   PZA03577.1   PZA03321.4   PHM3094.23   PZA01820.1   PZB00183.4   PZA02450.1 
## 1.595365e+02 1.608810e+02 1.608811e+02 2.421707e+02 2.438002e+02 2.438002e+02 
##   PHM10404.8   PZA01993.7   PZA02496.1   PZA02378.7   PZA02058.1   PZA01336.1 
## 2.465140e+02 2.465140e+02 2.465140e+02 2.465140e+02 2.475647e+02 2.483872e+02 
##   PHM4586.12   PZA01374.1   PZA01755.1   PZA03568.1   PHM1962.33   PZA03142.5 
## 2.483872e+02 2.497633e+02 2.512479e+02 2.512479e+02 2.543057e+02 2.543057e+02 
##   PZA02080.1   PZA01879.1   PZA00590.1   PZA03634.1    PHM6111.5   PZA03228.4 
## 2.543057e+02 2.560890e+02 2.564336e+02 2.578478e+02 2.578479e+02 2.588022e+02 
##   PZA00497.4 
## 2.596913e+02
(loglik.bb2[c] <- attr(population_Z006.bb2$geno[[c]]$map, "loglik"))
## [1] -2119.866
save.image("population_Z006.RData")

Grupo de ligação 5

c <- 5
plotRF(population_Z006, chr=c, col.scheme = "redblue")

population_Z006.bb1 <- orderMarkers(cross = population_Z006.bb1, chr=c, use.ripple = FALSE, map.function="kosambi")
plotRF(population_Z006.bb1, chr = c, col.scheme = "redblue")

pull.map(population_Z006.bb1, chr = c)
##    PZA03227.1   PZA02509.15    PHM1184.26    PHM2438.28    PHM3301.28 
##  0.000000e+00  5.000001e-06  1.000000e-05  4.279240e+00  1.261388e+01 
##    PZA00436.7    PZA00975.1    PZA00683.4    PZA02358.1    PZA02138.1 
##  1.459420e+01  1.940289e+01  1.940290e+01  3.218738e+01  3.218739e+01 
##    PZA01122.1    PZA02385.6     PHM8527.2    PZA00139.4    PZA01422.3 
##  3.218739e+01  3.218878e+01  3.311875e+01  3.787133e+01  3.787134e+01 
##   PZA03048.18    PZA02002.1    PZA02457.1    PZA02705.1   PHM15427.11 
##  3.935653e+01  3.935877e+01  4.052736e+01  4.407979e+01  4.407979e+01 
##    PZA01713.4   PHM13623.14    PHM5572.19    PZA03247.1    PZA01106.3 
##  4.574822e+01  4.574822e+01  4.574823e+01  4.574823e+01  4.574824e+01 
##    PZA00541.1    PZA03385.1    PZA01751.2   PZA00445.22 PZA00726.8.10 
##  4.574824e+01  4.606718e+01  4.606718e+01  4.673467e+01  4.705006e+01 
##    PHM1307.11    PZA01759.1    PHM14055.6       bt2.7.4    PZA03587.1 
##  4.705006e+01  4.705007e+01  4.705007e+01  4.768538e+01  4.800076e+01 
##    PZA03254.1    PZA02767.1    PZA00218.1    PZA03270.2    PZA03597.1 
##  4.800077e+01  4.831655e+01  4.831656e+01  4.831656e+01  4.831657e+01 
##    PZA03564.1    PZA03203.2    PZA03231.1    PZA00104.1    PZB00093.7 
##  4.897766e+01  4.963876e+01  5.029992e+01  5.029993e+01  5.343381e+01 
##    PZA00704.1    PZA03409.1    PZA02027.1        fea2.3    PZA03459.1 
##  5.482375e+01  5.482376e+01  5.552005e+01  5.552006e+01  5.586509e+01 
##    PZA02147.1    PZA03152.3   PZA02992.15    PZA02982.7    PZA00057.2 
##  5.745793e+01  5.919049e+01  5.948583e+01  5.948583e+01  6.381758e+01 
##    PZA03116.1    PZA01926.1    PZA00453.2    PHM3155.14    PZA01289.1 
##  6.841044e+01  6.841045e+01  7.588404e+01  7.742114e+01  7.742115e+01 
##    PZA00271.1    PZA01477.3    PZA01658.1    PZA01681.1  PZA03275.4.1 
##  8.241740e+01  8.276207e+01  8.276208e+01  8.276208e+01  8.473762e+01 
##    PZA01187.1    PZD00030.2    PZA01976.9    PZA01954.1    PZA02289.2 
##  9.006466e+01  9.115027e+01  9.254022e+01  9.254023e+01  9.254023e+01 
##    PHM3637.14    PZA02194.1    PZA00941.2    PZA01766.1   PZA00344.10 
##  9.254024e+01  9.254024e+01  9.996194e+01  9.996194e+01  1.006301e+02 
##    PZB01461.1    PZA00332.5    PHM4348.16    PZA00193.2    PZA01790.1 
##  1.018889e+02  1.027178e+02  1.039873e+02  1.039873e+02  1.054358e+02 
##    PZA03205.1    PZA01566.1    PZA01810.2    PZA02779.1    PZA01332.2 
##  1.057854e+02  1.061905e+02  1.061905e+02  1.065956e+02  1.065956e+02 
##    PZA02614.2    PZA02421.1    PZA02479.1    PZB01021.1    PZA03081.1 
##  1.065956e+02  1.065956e+02  1.070836e+02  1.070836e+02  1.070836e+02 
##    PZA00155.1    PZA03155.3    PZA00878.2    PZA01367.2    PZA00636.7 
##  1.075056e+02  1.085173e+02  1.090144e+02  1.090144e+02  1.090144e+02 
##    PZA00521.3    PZA00694.6    PZA02151.3    PZA02585.2    PHM5599.20 
##  1.107784e+02  1.114306e+02  1.148172e+02  1.151037e+02  1.151037e+02 
##    PZA00513.1    PZA00529.4    PHM5665.26    PHM2100.21  PZA03322.5.3 
##  1.151037e+02  1.210320e+02  1.212614e+02  1.252045e+02  1.252045e+02 
##    PHM4125.11   PZA01905.12    PZA03598.1 PZA00682.17.2   PZA02239.12 
##  1.274726e+02  1.333736e+02  1.333736e+02  1.396528e+02  1.396528e+02 
##   PZA00282.19 
##  1.402493e+02
(loglik.bb1[c] <- attr(population_Z006.bb1$geno[[c]]$map, "loglik"))
## [1] -1750.669
population_Z006.bb2 <- orderMarkers(cross = population_Z006.bb2, chr=c, use.ripple = FALSE, map.function="kosambi")
plotRF(population_Z006.bb2, chr = c, col.scheme = "redblue")

pull.map(population_Z006.bb2, chr = c)
##   PZA00282.19   PZA01905.12   PZA02239.12 PZA00682.17.2    PZA03598.1 
##     0.0000000     0.5965586     0.5965636     0.5965686     6.8764589 
##    PHM4125.11  PZA03322.5.3    PHM2100.21    PZA02585.2    PHM5665.26 
##    12.7773315    15.0453355    15.0455262    18.9932064    18.9932114 
##    PHM5599.20    PZA00529.4    PZA02151.3    PZA00513.1    PZA00694.6 
##    19.1071996    19.2223760    24.8159445    25.0497728    28.7435708 
##    PZA00521.3    PZA00636.7    PZA01367.2    PZA00878.2    PZA03155.3 
##    29.3940348    31.1529001    31.1529051    31.1529101    31.6503039 
##    PZA00155.1    PZA02479.1    PZA03081.1    PZB01021.1    PZA01332.2 
##    32.6627875    33.0809481    33.0809531    33.0809581    33.5631482 
##    PZA02779.1    PZA02614.2    PZA01566.1    PZA02421.1    PZA01810.2 
##    33.5631532    33.5631582    33.9640380    33.9640430    33.9640480 
##    PZA01790.1    PZA03205.1    PHM4348.16    PZA00193.2    PZA00332.5 
##    34.4538852    34.4538902    36.2025769    36.2025819    37.4737414 
##    PZB01461.1   PZA00344.10    PZA00941.2    PZA01976.9    PZA02289.2 
##    38.3036786    39.5641306    40.2324293    47.6035845    47.6035895 
##    PZA01954.1    PHM3637.14    PZA01766.1    PZA02194.1    PZD00030.2 
##    47.6035945    47.6035995    47.6036045    47.6036095    48.9866264 
##    PZA01187.1  PZA03275.4.1    PZA01658.1    PZA01681.1    PZA01477.3 
##    50.0671536    55.3623696    57.3295184    57.3295234    57.3295284 
##    PZA00271.1    PHM3155.14    PZA01289.1    PZA03116.1    PZA00453.2 
##    57.6728362    62.6440694    62.6440744    64.1753088    64.1753138 
##    PZA01926.1    PZA00057.2    PZA02147.1    PZA03152.3   PZA02992.15 
##    71.5826100    76.1449772    81.4147455    82.9257977    83.2127030 
##    PZA02982.7    PZA03459.1    PZA02027.1        fea2.3    PZA00704.1 
##    83.2127080    86.5848118    86.7420882    86.8988676    87.5343154 
##    PZA03409.1    PZA03231.1    PZB00093.7    PZA00104.1    PZA03203.2 
##    87.5343204    88.7754126    88.7754176    91.8924586    92.5488297 
##    PZA03564.1    PZA03270.2    PZA03597.1    PZA00218.1    PZA02767.1 
##    93.2054502    93.8620696    93.8620746    93.8620796    93.8620846 
##    PZA03587.1    PZA03254.1    PHM1307.11       bt2.7.4    PHM14055.6 
##    94.1772874    94.1772924    94.4921079    94.4921129    95.1260570 
##    PZA01759.1 PZA00726.8.10   PZA00445.22    PZA01751.2    PZA03385.1 
##    95.1260620    95.1260670    95.4408159    96.1069586    96.1069636 
##    PZA00541.1    PZA01106.3    PZA03247.1   PHM13623.14    PHM5572.19 
##    96.4250436    96.4250486    96.4250536    96.4250586    96.4250636 
##    PZA01713.4    PZA02705.1   PHM15427.11    PZA02457.1   PZA03048.18 
##    96.4250686    98.0877737    98.0877787   101.6265287   102.7951557 
##    PZA02002.1    PZA01422.3    PZA00139.4     PHM8527.2    PZA02385.6 
##   102.7951607   104.2799055   104.2799105   109.0323076   109.0351936 
##    PZA02358.1    PZA02138.1    PZA01122.1    PZA00975.1    PZA00436.7 
##   109.9632040   109.9632090   109.9632140   122.7108891   127.4834170 
##    PZA00683.4    PHM3301.28    PZA03227.1   PZA02509.15    PHM1184.26 
##   127.4834220   129.4489495   142.2469858   142.2469908   142.2469958 
##    PHM2438.28 
##   146.4434731
(loglik.bb2[c] <- attr(population_Z006.bb2$geno[[c]]$map, "loglik"))
## [1] -1793.54
save.image("population_Z006.RData")

Grupo de ligação 6

c <- 6
plotRF(population_Z006, chr=c, col.scheme = "redblue")

population_Z006.bb1 <- orderMarkers(cross = population_Z006.bb1, chr=c, use.ripple = FALSE, map.function="kosambi")
plotRF(population_Z006.bb1, chr = c, col.scheme = "redblue")

pull.map(population_Z006.bb1, chr = c)
##    PHM4711.14    PZA00368.1    PZA00416.7    PZA01601.1    PZA03178.1 
##  0.000000e+00  5.000001e-06  1.696468e+00  2.022070e+00  1.060627e+01 
##    PZA01691.1     PHM9695.8    PZA01951.1    PZA01079.1    PZA02955.3 
##  1.126837e+01  1.269359e+01  1.375280e+01  1.760666e+01  1.760666e+01 
##    PZA01357.2    PZA02528.1    PZA02454.2    PHM6428.11   PHM1978.111 
##  1.957484e+01  2.260872e+01  2.292831e+01  2.899991e+01  2.899991e+01 
##    PHM2350.17    PZA00758.1    PZA00498.5    PZA01186.1    PZA00793.2 
##  3.100519e+01  3.100519e+01  3.100520e+01  3.100520e+01  3.299124e+01 
##    PZA01196.2    PZA01209.1    PZA02203.1    PZA01301.1    PZA01470.1 
##  3.299125e+01  3.299125e+01  3.299126e+01  3.451841e+01  3.451841e+01 
##   PZA00717.15    PZA01960.1    PZA00379.2    PZA01257.1    PZA01363.2 
##  3.480853e+01  3.480853e+01  3.480854e+01  3.480854e+01  3.509507e+01 
##    PHM11114.7    PZA01297.1    PZA02522.1    PZA02683.1   PHM3978.104 
##  3.509507e+01  3.731949e+01  3.731950e+01  3.861280e+01  3.990609e+01 
##    PZA00908.2    PZA03135.1    PZA02019.1    PZA03579.1     PHM4134.8 
##  3.990610e+01  3.990610e+01  4.020742e+01  4.218234e+01  4.218234e+01 
##    PZA00739.1   PZA01972.14    PZA03012.7    PZA02566.1    PZA01072.1 
##  4.218235e+01  4.432735e+01  4.591140e+01  4.591141e+01  4.591141e+01 
##    PZA02748.3    PZA03638.1     PHM934.19    PZA03639.1    PZA03637.1 
##  4.640965e+01  4.640966e+01  4.640966e+01  4.640967e+01  4.640967e+01 
##    PHM3993.28    PZB02155.1    PZA01038.1 PHM10525.9.11  PZA00118.1.5 
##  4.677472e+01  5.115965e+01  5.146422e+01  5.146422e+01  5.176916e+01 
##    PZA01049.1    PZA01787.1    PHM5468.25  PZA03612.2.1   PHM14152.18 
##  5.244158e+01  5.244158e+01  5.244159e+01  5.244159e+01  5.436914e+01 
##    PZA00766.1    PZA00090.1    PZA02033.1    PHM4203.11     PHM448.23 
##  5.533879e+01  5.533880e+01  5.533880e+01  5.533881e+01  5.533881e+01 
##    PZA00770.1    PZA02011.1    PZA00429.1    PZA03651.1    PZA03650.1 
##  5.533882e+01  5.605015e+01  5.829062e+01  5.928465e+01  6.027513e+01 
##    PHM4757.14    PZA03698.1    PZA03182.5    PZA00951.1    PZA00838.2 
##  6.126796e+01  6.271470e+01  6.271471e+01  6.271471e+01  6.347332e+01 
##    PZA01741.1    PHM15278.6   PHM12749.13   PHM15623.10     PHM3465.6 
##  6.347333e+01  6.347333e+01  6.347334e+01  6.864830e+01  6.864831e+01 
##    PZA00675.1    PZB00811.1   PZA00706.16    PZA02746.2    PHM1834.47 
##  7.310476e+01  7.310477e+01  7.310477e+01  7.378847e+01  7.378847e+01 
##  PZA00460.3.8    PZA00362.1    PZA00020.5   PZA01964.29    PZA00904.1 
##  7.760690e+01  7.939066e+01  8.155461e+01  8.187176e+01  8.187177e+01 
##    PZA01316.1    PHM14046.9     PHM4786.9   PZA00189.23   PHM14104.23 
##  8.187177e+01  9.667448e+01  9.667461e+01  9.784082e+01  1.001624e+02 
##    PZA00760.1    PZA01290.1    PZA02281.3    PZA00071.2    PHM5019.59 
##  1.024833e+02  1.037345e+02  1.049805e+02  1.049805e+02  1.049805e+02 
##    PHM2749.10    PZA02388.1    PZA01600.2    PZA02174.2    PZA01623.3 
##  1.049805e+02  1.720588e+02  1.848777e+02  1.848777e+02  1.889861e+02 
##    PZA00058.1 
##  1.889861e+02
(loglik.bb1[c] <- attr(population_Z006.bb1$geno[[c]]$map, "loglik"))
## [1] -1638.694
population_Z006.bb2 <- orderMarkers(cross = population_Z006.bb2, chr=c, use.ripple = FALSE, map.function="kosambi")
plotRF(population_Z006.bb2, chr = c, col.scheme = "redblue")

pull.map(population_Z006.bb2, chr = c)
##    PZA00368.1    PHM4711.14    PZA00416.7    PZA01601.1    PZA03178.1 
##  0.000000e+00  5.000001e-06  1.696468e+00  2.022070e+00  1.060594e+01 
##    PZA01691.1     PHM9695.8    PZA01951.1    PZA01079.1    PZA02955.3 
##  1.126829e+01  1.269407e+01  1.375368e+01  1.760916e+01  1.760916e+01 
##    PZA01357.2    PZA02528.1    PZA02454.2   PHM1978.111    PZA00758.1 
##  1.957802e+01  2.261286e+01  2.293254e+01  2.901766e+01  3.102658e+01 
##    PHM6428.11    PZA01186.1    PHM2350.17    PZA01209.1    PZA01196.2 
##  3.102659e+01  3.102659e+01  3.301611e+01  3.301614e+01  3.301615e+01 
##    PZA00793.2    PZA02203.1    PZA00498.5    PZA01257.1    PZA01960.1 
##  3.301615e+01  3.301616e+01  3.301616e+01  3.423201e+01  3.423202e+01 
##    PZA00379.2    PHM11114.7   PZA00717.15    PZA01470.1    PZA01297.1 
##  3.423202e+01  3.423203e+01  3.423203e+01  3.451877e+01  3.674237e+01 
##    PZA02522.1    PZA01363.2    PZA02683.1    PZA01301.1   PHM3978.104 
##  3.674237e+01  3.674238e+01  3.674238e+01  3.674239e+01  3.940591e+01 
##    PZA03135.1    PZA00908.2    PZA02019.1    PZA03579.1     PHM4134.8 
##  3.940591e+01  3.940592e+01  3.970728e+01  4.168273e+01  4.168274e+01 
##    PZA00739.1    PZA02566.1   PZA01972.14    PZA01072.1    PZA03012.7 
##  4.168274e+01  4.382726e+01  4.382727e+01  4.541069e+01  4.541070e+01 
##     PHM934.19    PZA02748.3    PZA03637.1    PZA03639.1    PZA03638.1 
##  4.590909e+01  4.590909e+01  4.590910e+01  4.590910e+01  4.590911e+01 
##    PHM3993.28    PZB02155.1    PZA01038.1 PHM10525.9.11  PZA00118.1.5 
##  4.627421e+01  5.065887e+01  5.096338e+01  5.096338e+01  5.126826e+01 
##    PHM5468.25    PZA01787.1    PZA01049.1  PZA03612.2.1   PHM14152.18 
##  5.194058e+01  5.194059e+01  5.194059e+01  5.194060e+01  5.386791e+01 
##    PZA00766.1     PHM448.23    PZA00770.1    PZA00090.1    PHM4203.11 
##  5.483654e+01  5.483654e+01  5.483655e+01  5.483655e+01  5.483656e+01 
##    PZA02033.1    PZA02011.1    PZA00429.1    PHM4757.14    PZA03651.1 
##  5.483656e+01  5.554655e+01  5.778220e+01  6.089683e+01  6.089684e+01 
##    PZA03650.1    PZA03182.5    PZA03698.1    PZA00951.1    PZA00838.2 
##  6.089684e+01  6.234117e+01  6.234251e+01  6.234252e+01  6.310055e+01 
##    PHM15278.6    PZA01741.1   PHM12749.13    PZB00811.1     PHM3465.6 
##  6.310056e+01  6.310056e+01  6.310057e+01  6.827337e+01  6.827337e+01 
##    PZA00675.1   PHM15623.10   PZA00706.16    PHM1834.47  PZA00460.3.8 
##  7.272907e+01  7.272908e+01  7.272908e+01  7.341229e+01  7.723004e+01 
##    PZA02746.2    PZA00362.1    PZA00020.5     PHM4786.9    PZA00904.1 
##  7.723005e+01  7.901422e+01  8.117820e+01  8.149536e+01  8.149537e+01 
##    PZA01316.1    PHM14046.9   PZA01964.29   PHM14104.23   PZA00189.23 
##  8.149537e+01  9.628533e+01  9.628534e+01  9.745140e+01  9.745141e+01 
##    PZA00760.1    PHM2749.10    PZA01290.1    PHM5019.59    PZA02281.3 
##  1.023384e+02  1.048949e+02  1.048959e+02  1.048969e+02  1.048969e+02 
##    PZA00071.2    PZA02388.1    PZA02174.2    PZA01600.2    PZA01623.3 
##  1.048969e+02  1.719674e+02  1.847863e+02  1.847863e+02  1.888947e+02 
##    PZA00058.1 
##  1.888947e+02
(loglik.bb2[c] <- attr(population_Z006.bb2$geno[[c]]$map, "loglik"))
## [1] -1628.156
save.image("population_Z006.RData")

Grupo de ligação 7

c <- 7
plotRF(population_Z006, chr=c, col.scheme = "redblue")

population_Z006.bb1 <- orderMarkers(cross = population_Z006.bb1, chr=c, use.ripple = FALSE, map.function="kosambi")
plotRF(population_Z006.bb1, chr = c, col.scheme = "redblue")

pull.map(population_Z006.bb1, chr = c)
##     PZA00912.2     PZA02381.1     PZD00055.1     PHM4604.18   PZA01715.1.2 
##       0.000000       2.046563       6.488147       6.488152       6.488157 
##     PZA02197.1     PZB00221.3     PZA00323.3     PZA00511.3    PHM11226.13 
##       6.488162       6.488167       6.488172       6.488177       6.930740 
##      PHM816.29     PZA01369.1      PHM1766.1     PZA02252.2     PZA01096.1 
##       7.373281       8.265876      18.886154      18.886159      22.085765 
##     PZA02111.1     PZA01866.1      PHM4905.6     PZA02397.1     PZA03670.1 
##      26.875150      26.875155      26.875160      26.875165      26.875170 
##     PZA03671.1    PZA02235.14     PHM3330.25    PZA00213.19     PZA00060.2 
##      26.875175      26.875180      30.327795      30.657964      31.354653 
##     PZA00840.1     PZA02325.4     PZA03235.1     PZA02613.1     PZA01819.1 
##      31.354658      34.465533      34.465538      34.465543      34.465548 
##     PZA03470.1     PZB01358.1     PZA00015.5     PZA00152.1     PZA00225.8 
##      36.410096      36.869332      36.869337      37.965357      39.467259 
##     PHM3925.79     PZA00285.3      PHM1218.6   PZA01799.1.2     PZA01386.3 
##     102.685330     111.866436     115.158786     115.158791     118.078334 
##     PZA00466.1      sh1.12.11     PZA01195.3     PHM5181.10     PZA02344.1 
##     118.078339     118.078344     118.078349     121.242824     121.242829 
##     PZA02702.1     PZA03416.7          zb7.2      PHM9374.5     PZA00860.1 
##     126.987446     128.593052     129.222920     129.222925     129.222930 
## PZA03058.22.21     PZB01110.6     PZB00547.3     PZB00544.2     PZB01042.2 
##     129.223447     134.299388     134.299393     134.299398     134.299403 
##         ZHD1.1          wx1.1     PZA01999.3     PZB00540.3     PZB00959.1 
##     134.299408     134.299413     134.299418     134.299423     136.889945 
##     PZA02648.2     PZA00693.3     PZA03036.6    PZA02878.13    PZA00589.10 
##     136.889950     137.240304     137.590516     138.235972     138.235977 
##     PZA03469.1     PZB00014.1     PZA03057.3     PZA01791.2     PZA00925.2 
##     138.235982     138.235987     139.546253     139.546258     139.546263 
##     PZB00761.1     PZA01861.1     PZA03596.1     PZA01062.1     PZA02545.1 
##     140.124987     140.412610     140.995284     140.995289     140.995294 
##     PZA00947.1     PZA01281.2     PZB01899.1     PHM4303.16    PHM15445.25 
##     142.482339     142.482344     143.973911     203.856871     205.900723 
##     PZA00832.1     PZA00708.3    PHM1911.173    PHM13681.12     PZA03573.1 
##     205.900728     207.050036     214.022403     214.022446     222.747497
(loglik.bb1[c] <- attr(population_Z006.bb1$geno[[c]]$map, "loglik"))
## [1] -1538.77
population_Z006.bb2 <- orderMarkers(cross = population_Z006.bb2, chr=c, use.ripple = FALSE, map.function="kosambi")
plotRF(population_Z006.bb2, chr = c, col.scheme = "redblue")

pull.map(population_Z006.bb2, chr = c)
##     PZA00912.2     PZA02381.1     PZB00221.3     PZA00511.3     PZA00323.3 
##       0.000000       2.046563       6.488243       6.488248       6.488253 
##     PHM4604.18   PZA01715.1.2     PZA02197.1     PZD00055.1    PHM11226.13 
##       6.488258       6.488263       6.631429       6.774638       6.927410 
##      PHM816.29     PZA01369.1      PHM1766.1     PZA02252.2     PZA01096.1 
##       7.370682       8.263302      18.881378      18.881383      22.068802 
##    PZA02235.14     PZA02397.1     PZA03670.1     PZA02111.1     PZA01866.1 
##      22.068807      26.959519      26.959524      26.959529      26.959534 
##     PZA03671.1      PHM4905.6     PHM3330.25    PZA00213.19     PZA00060.2 
##      26.959539      26.959544      30.474343      30.809586      31.515313 
##     PZA00840.1     PZA02613.1     PZA03235.1     PZA02325.4     PZA01819.1 
##      31.515318      34.779166      34.779171      34.779176      34.779181 
##     PZA03470.1     PZB01358.1     PZA00015.5     PZA00152.1     PZA00225.8 
##      36.874717      37.376326      37.376331      38.679009      40.633319 
##     PZB01899.1     PZA01281.2     PZA00947.1     PZA01062.1     PZA03596.1 
##      44.987092      46.779576      46.779581      48.508391      48.508396 
##     PZA02545.1     PZA01861.1     PZB00761.1     PZA03057.3     PZA00925.2 
##      48.508401      49.137508      49.445941      50.067088      50.067093 
##     PZA01791.2     PZA03469.1    PZA02878.13     PZB00014.1    PZA00589.10 
##      50.067098      51.451614      51.451619      51.451624      51.451629 
##     PZA03036.6     PZA00693.3     PZB00959.1     PZA02648.2     PZB01110.6 
##      52.132462      52.492720      52.670991      52.851761      52.852717 
##          wx1.1          zb7.2     PZB00547.3     PZB01042.2     PZB00544.2 
##      55.500126      55.500131      55.500136      55.500141      55.500146 
##         ZHD1.1     PZA01999.3     PZB00540.3 PZA03058.22.21      PHM9374.5 
##      55.500151      55.500156      55.500161      55.500166      60.658220 
##     PZA00860.1     PZA03416.7     PZA02702.1     PZA02344.1     PZA01386.3 
##      60.658225      61.290927      62.905395      68.692250      68.692255 
##     PHM5181.10      sh1.12.11     PZA00466.1     PZA01195.3      PHM1218.6 
##      68.692260      71.856585      71.856590      71.856595      71.856600 
##   PZA01799.1.2     PZA00285.3     PHM3925.79     PHM4303.16     PZA00832.1 
##      74.775020      78.066174      87.245598     153.597793     155.645090 
##     PZA00708.3    PHM15445.25    PHM13681.12    PHM1911.173     PZA03573.1 
##     156.796434     156.796439     163.768283     163.768288     172.493422
(loglik.bb2[c] <- attr(population_Z006.bb2$geno[[c]]$map, "loglik"))
## [1] -1472.952
save.image("population_Z006.RData")

Grupo de ligação 8

c <- 8
plotRF(population_Z006, chr=c, col.scheme = "redblue")

population_Z006.bb1 <- orderMarkers(cross = population_Z006.bb1, chr=c, use.ripple = FALSE, map.function="kosambi")
plotRF(population_Z006.bb1, chr = c, col.scheme = "redblue")

pull.map(population_Z006.bb1, chr = c)
##    PZA01875.1   PZA02815.25    PHM4468.13    PZA00910.1    PZA02141.1 
##      0.000000      1.064806      1.064811      3.616106      9.299753 
##    PZB01222.1    PZA02688.2    PZA00821.1    PZA00889.2     PHM7922.8 
##     10.335187     10.661474     11.319019     13.530160     13.819686 
##    PZA01468.1    PZA00266.7    PHM4503.25    PZB01569.7    PHM4748.16 
##     24.954418     24.954423     24.954428     25.277673     32.732743 
##    PZA00223.4    PHM5794.13    PZA02436.1    PZA01672.1   PZA03027.12 
##     36.440701     36.440706     37.071844     37.071849     37.071854 
##    PZA01462.1    PZA01342.2    PZA01144.1   PHM11985.27    PZA02247.1 
##     37.072066     48.749643     49.681592     49.985589     50.811951 
##    PZA03102.9    PZB00942.1    PZB01308.1    PZA02148.1    PZA02673.1 
##     51.635326     51.976720     51.976725     51.976730     51.976735 
##    PZA02328.5    PZA02478.7  PZA02187.1.2   PZA00357.19    PZA02262.3 
##     53.382015     53.382020     53.382025     53.696075     53.696080 
##    PZA01884.1    PZB00414.2    PZA01618.2    PZA00473.5    PZA01591.1 
##     54.688984     55.333214     57.679363     57.679368     57.679373 
##    PZA01552.1    PZA01729.1   PHM13020.10    PZA01055.1        lac1.3 
##     57.679378     57.994290     57.994295     58.628418     58.628423 
##    PZA00571.1   PZA00382.17    PZA01736.1   PZA02396.14    PZA01029.1 
##     58.628428     58.941328     58.941333     60.926856     60.926861 
##    PZA02048.2    PZA01589.2    PZA03461.1    PZA00942.2    PZB01658.1 
##     60.926866     60.926871     66.836671     74.746601     74.746606 
##    PHM8909.12    PZA03488.1   PHM15961.13    PZA00158.2 PZA00440.15.1 
##     79.631013     79.631018    102.857367    109.339227    111.423654 
##   PZA03047.12   PZA03063.21   PZA02948.24    PZA03120.1    PZA01901.1 
##    111.423659    111.737582    111.737587    112.068039    112.068044 
##    PZA01509.1    PZA01527.1    PZA01425.2    PZA00427.3    PZA00355.2 
##    112.068049    112.690626    115.421779    115.734367    115.734372 
##   PZA00543.12  PZA03069.8.4    PZA02606.1    PZA00214.1    PHM2551.31 
##    116.017859    116.297963    116.583534    118.046066    118.046071 
##   PZA00006.17  PZB01009.2.1    PZD00072.2 
##    118.046076    118.046081    118.046086
(loglik.bb1[c] <- attr(population_Z006.bb1$geno[[c]]$map, "loglik"))
## [1] -1280.004
population_Z006.bb2 <- orderMarkers(cross = population_Z006.bb2, chr=c, use.ripple = FALSE, map.function="kosambi")
plotRF(population_Z006.bb2, chr = c, col.scheme = "redblue")

pull.map(population_Z006.bb2, chr = c)
##    PHM4468.13    PZA01875.1    PZA00910.1   PZA00006.17  PZB01009.2.1 
##  0.000000e+00  5.000001e-06  1.448485e+00  6.536965e+01  6.536966e+01 
##    PZA00214.1    PHM2551.31    PZD00072.2    PHM8909.12    PZA02606.1 
##  6.536966e+01  6.536967e+01  6.536967e+01  6.536968e+01  6.683037e+01 
##  PZA03069.8.4   PZA00543.12    PZA00427.3    PZA00355.2    PZA01425.2 
##  6.711560e+01  6.739627e+01  6.767986e+01  6.767987e+01  6.799277e+01 
##    PZA01527.1   PZA03047.12   PZA03063.21   PZA02948.24    PZA01901.1 
##  7.070635e+01  7.175999e+01  7.183803e+01  7.183804e+01  7.213541e+01 
##    PZA01509.1    PZA03120.1 PZA00440.15.1    PZA00158.2   PHM15961.13 
##  7.213542e+01  7.213542e+01  7.275440e+01  7.481938e+01  8.128466e+01 
##    PZA03488.1    PZA00942.2    PZB01658.1    PZA03461.1    PZA02048.2 
##  1.045381e+02  1.094419e+02  1.130662e+02  1.166857e+02  1.226375e+02 
##    PZA01589.2   PZA02396.14    PZA01029.1    PZA01736.1   PZA00382.17 
##  1.226375e+02  1.226375e+02  1.226375e+02  1.246275e+02  1.246275e+02 
##    PZA00571.1        lac1.3    PZA01055.1   PHM13020.10    PZA01729.1 
##  1.249408e+02  1.249408e+02  1.249408e+02  1.255758e+02  1.255758e+02 
##    PZA01552.1    PZA01618.2    PZA01591.1    PZA00473.5    PZB00414.2 
##  1.258912e+02  1.258912e+02  1.258912e+02  1.258912e+02  1.282427e+02 
##    PZA01884.1   PZA00357.19    PZA02262.3    PZA02328.5    PZB01308.1 
##  1.288883e+02  1.298845e+02  1.298845e+02  1.301993e+02  1.316003e+02 
##    PZA02478.7  PZA02187.1.2    PZA02673.1    PZB00942.1    PZA02148.1 
##  1.316003e+02  1.316003e+02  1.316003e+02  1.316003e+02  1.316003e+02 
##    PZA03102.9    PZA01144.1   PHM11985.27    PZA02247.1    PZA01462.1 
##  1.319406e+02  1.338088e+02  1.339718e+02  1.339718e+02  1.352249e+02 
##    PZA01342.2    PZA01672.1   PZA03027.12    PZA02436.1    PHM5794.13 
##  1.352249e+02  1.403847e+02  1.455444e+02  1.455444e+02  1.461837e+02 
##    PZA00223.4    PHM4748.16    PZB01569.7    PHM4503.25    PZA01468.1 
##  1.461838e+02  1.499468e+02  1.575002e+02  1.578259e+02  1.578259e+02 
##    PZA00266.7    PZA00889.2     PHM7922.8    PZA00821.1    PZA02688.2 
##  1.578259e+02  1.685691e+02  1.687816e+02  1.712741e+02  1.719344e+02 
##    PZA02141.1    PZB01222.1   PZA02815.25 
##  1.727208e+02  1.733880e+02  1.813890e+02
(loglik.bb2[c] <- attr(population_Z006.bb2$geno[[c]]$map, "loglik"))
## [1] -1422.552
save.image("population_Z006.RData")

Grupo de ligação 9

c <- 9
plotRF(population_Z006, chr=c, col.scheme = "redblue")

population_Z006.bb1 <- orderMarkers(cross = population_Z006.bb1, chr=c, use.ripple = FALSE, map.function="kosambi")
plotRF(population_Z006.bb1, chr = c, col.scheme = "redblue")

pull.map(population_Z006.bb1, chr = c)
##   PZA02365.7  PZA00616.13   PZA01946.7   PZA01690.7   PZA00986.1   PZA02352.1 
##     0.000000     1.638188     1.841656     1.841661     2.140298     3.527336 
##   PZA01714.1   PZA02643.1   PZB00752.1   PZA03583.1  PZA00111.10   PZA00740.3 
##     9.494086     9.494091    11.543476    11.543481    12.208108    12.208113 
##   PZA01542.1   PZD00054.1  PZA02449.13  PZA02984.10   PZA03166.1   PZA03728.1 
##    12.208118    12.208123    12.554383    12.900551    14.159505    14.294844 
##  PZA02854.13  PHM9162.135 PZA00405.7.6  PHM16437.20   PZA02722.1   PZA03176.4 
##    14.630991    14.965787    15.263651    19.187208    19.834479    25.450478 
##   PZA02386.2   PZA02260.2     PHM112.8   PZA00795.1   PHM7898.10   PZA02373.1 
##    28.706429    28.706434    28.706439    40.990988    44.998670    44.998675 
##   PZA01533.2   PZA02223.2  PHM10225.15   PZA00505.6   PZA01802.3   PZA01414.1 
##    45.633368    45.633373    45.633378    46.936296    47.792904    48.518255 
##   PZA00386.4   PZA01028.2   PZA00695.3   PZA01278.2   PZB00605.1   PZA02274.1 
##    50.271165    50.271170    53.353541    54.002635    54.002640    68.024225 
##   PZA00424.1   PZA01744.1   PZA01044.1   PZA01426.1   PZA02035.5   PHM9241.13 
##    68.024230    68.024235    68.024240   130.036910   135.761626   147.715931 
##   PHM3078.12 PZA01909.1.2   PZA02872.1   PHM4080.15  PZA00256.27   PZA03624.1 
##   149.882834   152.050777   166.284212   167.848087   169.407528   170.969640 
##   PZA03344.2   PHM15501.9   PHM4353.31  PZA00132.17   PZA03687.1   PZA00084.2 
##   172.345520   174.631133   174.631138   174.631143   174.932768   175.629918 
##   PZA01936.4   PZA01230.1   PZA02612.1   PZA03363.1   PZA03723.1  PHM12830.14 
##   175.629923   175.629928   175.629933   175.629938   175.629943   175.973898 
## PZA01210.1.2    PHM904.21   PZA01445.1   PZA03645.1   PZA02291.1   PHM4818.15 
##   175.973903   175.973908   175.973913   175.973918   175.973923   175.973928 
##   PZA00418.2   PZA01607.1   PZA02018.1   PZA01113.1   PZA01933.3   PZA02236.1 
##   175.973933   175.973938   176.800129   176.800134   177.638805   178.479773
(loglik.bb1[c] <- attr(population_Z006.bb1$geno[[c]]$map, "loglik"))
## [1] -1439.1
population_Z006.bb2 <- orderMarkers(cross = population_Z006.bb2, chr=c, use.ripple = FALSE, map.function="kosambi")
plotRF(population_Z006.bb2, chr = c, col.scheme = "redblue")

pull.map(population_Z006.bb2, chr = c)
##   PZA02260.2   PZA00795.1   PZA02373.1   PHM7898.10   PZA02223.2  PHM10225.15 
##      0.00000     12.30822     16.31563     16.31564     16.62915     16.94586 
##   PZA01533.2   PZA00505.6   PZA01802.3   PZA01414.1   PZA00386.4   PZA01028.2 
##     18.24663     18.24868     19.10545     19.83083     21.58384     21.58385 
##   PZA00695.3   PZB00605.1   PZA00424.1   PZA01278.2   PZA02274.1   PZA01044.1 
##     24.66623     25.31532     25.31533     25.31533     39.33676     39.33676 
##   PZA01744.1   PZA01426.1   PZA02035.5   PHM9241.13 PZA01909.1.2   PHM3078.12 
##     39.33677    101.33775    107.06253    119.00470    123.55198    123.55198 
##   PZA02872.1  PZA00256.27   PZA03624.1   PHM4080.15   PZA03344.2   PHM15501.9 
##    137.85786    142.84632    142.84632    142.84633    144.20325    146.46451 
##   PHM4353.31  PZA00132.17   PZA03687.1   PZA01230.1   PZA00084.2   PZA02612.1 
##    146.46452    146.46452    146.76362    147.45308    147.45308    147.45309 
##   PZA03363.1   PZA01936.4   PZA03723.1   PZA02291.1   PZA03645.1   PZA00418.2 
##    147.45309    147.45310    147.45310    147.80519    147.80519    147.80520 
##   PHM4818.15   PZA01445.1    PHM904.21  PHM12830.14 PZA01210.1.2   PZA01607.1 
##    147.80520    147.80521    147.80521    147.80522    147.80522    147.80523 
##   PZA02018.1   PZA01933.3   PZA01113.1   PZA02236.1   PZA02365.7   PZA00986.1 
##    148.65583    148.65583    148.65584    150.52316    158.99871    160.82503 
##   PZA01690.7  PZA00616.13   PZA01946.7   PZA02352.1   PZA03583.1   PZA02643.1 
##    160.98218    160.98218    160.98219    162.77795    168.74897    168.74897 
##   PZA01714.1   PZA01542.1   PZB00752.1   PZD00054.1   PZA00740.3  PZA00111.10 
##    168.74898    170.79557    170.79557    171.12525    171.45492    171.45492 
##  PZA02984.10  PZA02449.13   PZA03728.1  PZA02854.13   PZA03166.1  PHM9162.135 
##    171.89080    171.89080    173.30144    173.30144    173.30145    173.98279 
## PZA00405.7.6  PHM16437.20   PZA02722.1   PZA03176.4   PZA02386.2     PHM112.8 
##    174.27957    178.18530    178.82892    184.46331    187.73542    187.73542
(loglik.bb2[c] <- attr(population_Z006.bb2$geno[[c]]$map, "loglik"))
## [1] -1528.935
save.image("population_Z006.RData")

Grupo de ligação 10

c <- 10
plotRF(population_Z006, chr=c, col.scheme = "redblue")

population_Z006.bb1 <- orderMarkers(cross = population_Z006.bb1, chr=c, use.ripple = FALSE, map.function="kosambi")
plotRF(population_Z006.bb1, chr = c, col.scheme = "redblue")

pull.map(population_Z006.bb1, chr = c)
##   PZA02554.1   PZA02527.2  PZA02221.20   PZA01883.2   PZA01313.2   PZA01451.1 
##       0.0000     385.6237     771.2474     797.4327     797.4328     797.4329 
##  PZA02095.10   PHM3631.47   PHM2828.83    PHM3765.7   PZB01301.5  PHM15331.16 
##     797.4329     797.4329     797.4329     797.4329     798.9351     800.5173 
##   PZA00463.3    PHM3896.9   PZA01642.1   PHM3922.32   PZA02961.6   PZA00079.1 
##     804.2684     804.9748     804.9748     806.3717     806.3717     807.0921 
##   PZD00033.3   PZA02470.2  PZA02853.11   PZA03491.1   PZA01597.1  PZA00409.17 
##     810.1143     810.1143     810.1143     810.4790     810.4790     810.4790 
##   PZA00933.3   PZA02941.7   PZA01677.1   PHM2770.19   PZA01877.2   PZB00409.6 
##     811.2323     811.2323     811.2323     811.7916     812.3591     813.3439 
##   PZA00337.4   PZA00814.1  PHM12990.15   PZA01619.1    PHM537.22   PZA00444.1 
##     813.8281     814.1725     814.1725     815.0808     816.0712     816.0712 
##   PZA00400.3   PZA00048.1   PZA01919.2   PZA01292.1   PHM18195.6   PZA02398.2 
##     816.1321     816.1321     816.4472     816.4472     816.4472     816.4472 
##  PHM12625.18   PZA02128.3   PHM4341.42   PZA01089.1   PZA02219.2   PZA03713.1 
##     816.4472     816.7672     818.3377     818.3377     819.1275     819.9174 
##   PZA01141.1  PHM13687.14   PZA03196.1   PZA00866.2   PZA01005.1   PZA00647.9 
##     819.9174     819.9174     821.4934     821.8445     821.8445     826.1615 
##   PZA01241.2   PZA02320.1   PZB01111.8   PZA01456.2   PZA02663.1  PHM15868.56 
##     827.7143     829.5817     832.5351     835.9832     838.0331     838.0331 
## PHM18513.156   PZA01995.2   PZA03605.1   PZA03604.1   PZA03606.1   PZA03603.1 
##     838.0331     841.2003     844.0188     844.0188     844.0188     844.0188 
##   PZA02969.9   PZA00130.9   PZA03607.1   PZA00007.1   PZA02049.1   PHM5435.25 
##     848.9793     848.9793     848.9793     849.5948     849.5948     849.9805 
##   PZA01001.2   PZA01073.1   PZA02167.2   PZA00062.4   PZA02578.1 
##     851.1195     851.1195     851.1195     859.7716     862.1558
(loglik.bb1[c] <- attr(population_Z006.bb1$geno[[c]]$map, "loglik"))
## [1] -1347.615
population_Z006.bb2 <- orderMarkers(cross = population_Z006.bb2, chr=c, use.ripple = FALSE, map.function="kosambi")
plotRF(population_Z006.bb2, chr = c, col.scheme = "redblue")

pull.map(population_Z006.bb2, chr = c)
##   PZA02554.1   PZA02527.2  PZA02095.10  PZA02221.20   PZA01451.1   PZA01883.2 
##       0.0000     385.6237     771.2474     771.2474     797.4285     797.4285 
##   PHM2828.83    PHM3765.7   PHM3631.47   PZA01313.2   PZB01301.5  PHM15331.16 
##     797.4285     797.4285     797.4285     797.4285     798.9308     800.5129 
##   PZA00463.3    PHM3896.9   PZA01642.1   PHM3922.32   PZA02961.6   PZA00079.1 
##     804.2633     804.9698     804.9698     806.3667     806.3667     807.0870 
##   PZD00033.3   PZA02470.2  PZA02853.11  PZA00409.17   PZA03491.1   PZA01597.1 
##     810.1141     810.1141     810.1141     810.4795     810.4795     810.4796 
##   PZA00933.3   PZA02941.7   PZA01677.1   PHM2770.19   PZA01877.2   PZB00409.6 
##     811.2325     811.2325     811.2325     811.7946     812.3587     813.3431 
##   PZA00337.4  PHM12990.15   PZA00814.1   PZA01619.1   PZA00048.1   PZA00400.3 
##     813.8271     814.1713     814.1714     815.0867     815.5335     815.9808 
##   PZA01292.1  PHM12625.18   PZA00444.1   PZA01919.2    PHM537.22   PZA02398.2 
##     816.3144     816.3144     816.3144     816.3144     816.3144     816.3144 
##   PHM18195.6   PZA02128.3   PHM4341.42   PZA01089.1   PZA02219.2   PZA03713.1 
##     816.3144     816.6339     818.1918     818.1918     818.9742     819.7567 
##  PHM13687.14   PZA01141.1   PZA00866.2   PZA01005.1   PZA03196.1   PZA00647.9 
##     819.7567     819.7567     821.7122     821.7122     821.9972     826.6024 
##   PZA01241.2   PZA02320.1   PZB01111.8   PZA01456.2  PHM15868.56 PHM18513.156 
##     828.1374     830.0027     832.9516     836.3967     838.4458     838.4458 
##   PZA02663.1   PZA01995.2   PZA03605.1   PZA03603.1   PZA03606.1   PZA03604.1 
##     838.4458     841.6127     844.4309     844.4309     844.4309     844.4309 
##   PZA02969.9   PZA00130.9   PZA03607.1   PZA00007.1   PZA02049.1   PHM5435.25 
##     849.3917     849.3917     849.3917     850.0072     850.0072     850.3930 
##   PZA01073.1   PZA01001.2   PZA00062.4   PZA02167.2   PZA02578.1 
##     851.5320     851.5321     860.1840     862.5705     862.5705
(loglik.bb2[c] <- attr(population_Z006.bb2$geno[[c]]$map, "loglik"))
## [1] -1344.009
save.image("population_Z006.RData")

Ajustes manuais da saída orderMarkers()

Antes de quaisquer ajustes manuais nos pedidos de marcadores, precisamos verificar quais corridas de desempenho melhor para cada grupo de linkage. Para isso, devemos olhar para os comprimentos do grupo de ligação, espaço máximo entre dois marcadores consecutivos (também conhecido como lacuna) e suas probabilidades de log. Maiores probabilidades de log indicam mapas melhores (ou seja, mais prováveis).orderMarkers()

Combinamos as informações da função com a recolhida nos objetos e de cada execução:summaryMap()loglik.bb1loglik.bb1

knitr::kable(cbind(summaryMap(population_Z006.bb1), log.likelihood=c(loglik.bb1, sum(loglik.bb1))))
n.mar length ave.spacing max.spacing log.likelihood
1 175 666.0536 3.827894 385.623712 -2839.988
2 139 150.3227 1.089295 15.755731 -1670.498
3 130 157.8291 1.223481 9.164913 -1926.808
4 127 259.1320 2.056603 81.258438 -2125.426
5 111 140.2493 1.274994 12.784481 -1750.669
6 106 188.9861 1.799868 67.078293 -1638.694
7 85 222.7475 2.651756 63.218071 -1538.770
8 78 118.0461 1.533066 23.226349 -1280.004
9 78 178.4798 2.317919 62.012670 -1439.100
10 77 862.1558 11.344155 385.623712 -1347.615
overall 1106 2944.0020 2.686133 385.623712 -17557.572
plotMap(population_Z006.bb1)

save.image("population_Z006.RData")
plotRF(population_Z006.bb1, col.scheme = "redblue")

save.image("population_Z006.RData")
knitr::kable(cbind(summaryMap(population_Z006.bb2), log.likelihood=c(loglik.bb2, sum(loglik.bb2))))
n.mar length ave.spacing max.spacing log.likelihood
1 175 668.1498 3.839941 385.62371 -2934.306
2 139 149.8523 1.085886 15.75728 -1667.511
3 130 544.4887 4.220843 385.62371 -2089.495
4 127 259.6913 2.061042 81.28966 -2119.866
5 111 146.4435 1.331304 12.79804 -1793.540
6 106 188.8947 1.798997 67.07050 -1628.156
7 85 172.4934 2.053493 66.35220 -1472.952
8 78 181.3890 2.355702 63.92117 -1422.552
9 78 187.7354 2.438122 62.00098 -1528.935
10 77 862.5705 11.349612 385.62371 -1344.009
overall 1106 3361.7087 3.067252 385.62371 -18001.323
plotMap(population_Z006.bb2)

save.image("population_Z006.RData")
plotRF(population_Z006.bb2, col.scheme = "redblue")

save.image("population_Z006.RData")
save.image("population_Z006_bb.RData")

Agora, podemos selecionar a melhor ordem até agora, que ainda podemos tentar melhorar fazendo ajustes manuais para cada grupo de linkage. Como estratégia para melhorar o pedido de marcadores, vamos encontrar onde estão as principais lacunas, e corrigi-lo movendo o bloco de marcadores para sua posição mais provável quando olhar para o mapa de calor.

E podemos continuar fazendo isso por cada grupo de ligação. No entanto, outra alternativa oferece melhor encomenda em geral de forma muito mais eficiente.

Escala Multidimensional (MDS) por MDSMap + MAPpoly

E podemos continuar fazendo isso por cada grupo de ligação. No entanto, outra alternativa oferece melhor encomenda em geral de forma muito mais eficiente.

library(mappoly)
getMDSorder <- function(cross, chr){
  markers <- match(names(cross$geno[[chr]]$map), colnames(cross$rf))
  mat <- cross$rf[markers,markers]
  rec.mat <- lod.mat <- matrix(rep(NA, length(markers)^2), nrow = length(markers))
  colnames(rec.mat) <- colnames(lod.mat) <- rownames(rec.mat) <- rownames(lod.mat) <- colnames(mat)
  lod.mat[upper.tri(lod.mat)] <- mat[upper.tri(mat)]
  lod.mat[lower.tri(lod.mat)] <- t(lod.mat)[lower.tri(lod.mat)]
  rec.mat[lower.tri(rec.mat)] <- mat[lower.tri(mat)]
  rec.mat[upper.tri(rec.mat)] <- t(rec.mat)[upper.tri(rec.mat)]
  input.mat <- NULL
  input.mat$rec.mat <- rec.mat
  input.mat$lod.mat <- lod.mat
  mds.map <- mappoly::mds_mappoly(input.mat)
  mds.ord <- match(as.character(mds.map$locimap$locus), colnames(mat))
  return(mds.ord)
}

Criaremos um novo objeto chamado que é uma cópia do nosso objeto cruzado original, para que possamos atualizar o pedido apenas dentro. Além disso, criaremos um objeto vazio chamado para armazenar a probabilidade de registro dos pedidos obtidos usando MDS:population_Z006.mds, loglik.mds

Grupo de ligação 1

population_Z006.mds <- population_Z006
loglik.mds <- c()
c <- 1
mds.ord <- getMDSorder(cross = population_Z006.mds, chr = c)
## Stress: 0.68318
## Mean Nearest Neighbour Fit: 4.36971
population_Z006.mds <- switch.order(cross = population_Z006.mds, chr=c, order=mds.ord, maxit = 10000, tol=1e-5)
plotRF(population_Z006.mds, chr=c, col.scheme = "redblue")

pull.map(population_Z006.mds, chr = c)
##     PZA03613.1     PZA01271.1     PZA02129.1     PZA02032.1    PHM2244.142 
##      0.0000000      0.0000005      2.5282661      3.4054769      9.0730801 
##     PZA02372.1     PHM6238.36     PZA00181.2     PZA00528.1     PZA00175.2 
##     11.5666722     12.9262457     14.7638228     14.7638233     14.7638238 
##     PZA00447.8     PZA02284.1     PZA00731.7     PZA00566.5    PZA00106.10 
##     19.6939074     19.6939079     19.6939084     21.7818794     22.7099972 
##     PZA03521.1     PZA00887.1     PZA03551.1      csu1171.2     PZA01497.1 
##     22.7099977     22.7099982     24.8408474     31.2601476     31.2601481 
##     PZA01652.1     PZA02094.9     PZA02393.2     PZB00648.5     PZB00718.5 
##     31.2601486     31.2601491     35.0118887     36.5890863     36.5890868 
##     PZA01030.1    PZA00425.11     PHM13619.5     PZA02487.1     PHM3226.15 
##     38.4494562     46.4399030     46.4399035     49.7022766     49.7022771 
##     PHM4531.46     PZB01957.1     PZA02490.1     PZB02058.1     PZA01348.1 
##     49.7022776     49.7022781     54.4478392     54.4478397     54.4478402 
##     PZB01662.1     PZA01455.1     PZA02686.1     PZA02271.1     PZA02195.1 
##     54.4478407     54.4478412     54.4478417     58.9373794     59.2623925 
##     PZA00240.6    PHM3726.129     PZA00962.1     PZA02376.1     PZA03742.1 
##     62.3224685     62.3224690     62.6720715     64.0622494     64.0622499 
##     PZA03243.2    PZA00081.18        umc13.1     PZA03183.5     PZB00872.3 
##     64.0622504     65.0198930     65.6367743     65.9589642     65.9590483 
##     PHM4913.18     PZA02292.1     PZA03168.5     PZA02737.1     PZA02550.1 
##     67.7825758     67.7825763     67.7825768     71.6844270     72.0538764 
##     PZA02114.1     PZB01062.3     PZA03561.1     PZA01315.1     PZA01476.1 
##     72.3929764     72.3929769     72.3929774     73.3581948     73.6733659 
##    PZA00294.22     PZA03189.4     PHM5098.25     PZA01267.3     PZA00752.1 
##     74.6808263     75.0328079     81.8706476     81.8706481     83.9793935 
##     PZA01135.1   PZA03240.1.2     PZA03465.1   PZA00944.1.2     PZB01235.4 
##     85.5832681     88.5922178     88.9403654     88.9403659     91.1611242 
##     PZA02763.1     PZA00939.1     PHM9418.11     PZA02750.3     PZA01254.2 
##     93.4463059     93.4463064     93.4463069     93.4463074     93.4463079 
##     PZA02070.1    csu1138.3.4     PZA02577.1     PZA03200.2     PZA02135.2 
##     94.2117952     94.9131166     94.9131171     95.2592856    100.9524308 
##     PZA02741.1          an1.5 PZA00455.14.16     PZA02191.1     PHM1968.22 
##    100.9524313    100.9524318    104.9853948    109.1072122    109.1072127 
##     PZA00068.1     PZA03531.1     PZA00619.3    PZA02467.10    PZA03074.27 
##    109.7473717    109.7473722    109.7473727    123.3757522    127.7732373 
##     PZA01216.1    PZA00131.15     PZA01391.1     PHM5480.17     PZA03194.1 
##    129.9270022    129.9270027    129.9270032    129.9270037    129.9270042 
##    PZA01963.15     PZA01019.1     PZA03193.2    PHM12706.14     PZA01039.1 
##    129.9270047    129.9270052    137.9938812    138.5903335    138.5903340 
##     PZA02014.3     PZA03265.3     PHM6043.19     PZA03741.1     PHM5484.22 
##    138.5903345    138.5903350    141.6175165    142.2431183    145.6438131 
##    PHM15871.11     PZA02117.1     PZA02823.1    PZA00658.21     PHM2478.22 
##    145.6438136    147.1558071    147.7518395    147.7518400    147.7518405 
##     PHM4942.12       umc128.2     PZA00664.3     PZA02186.1     PZB01647.1 
##    147.7518410    147.7518415    147.7518420    148.8591554    148.8591559 
##    PZA03001.15     PZA00381.4     PZA03301.2     PHM4926.16     PZA03064.6 
##    148.8591564    156.6769060    156.6769065    156.6769070    161.5703925 
##    PHM16605.19     PZA03404.1   PZA02269.3.4      PHM3034.3         kip1.3 
##    162.2774938    164.4884317    164.4884322    167.3612058    167.3612063 
##     PHM14475.7         glb1.2     PZA01588.1     PZA00339.4 PZA01921.20.19 
##    168.4749388    174.5347258    174.5347263    174.5347268    174.5347273 
##     PHM5526.25     PZA02985.5     PZA03457.1     PZB00008.1     PZB00895.1 
##    174.5347278    174.5347283    175.6972680    175.6972685    175.6972690 
##     PZB00063.1     PZA02278.1     PZA02698.3    PZA00030.11     PZA02520.1 
##    175.6972695    182.7205995    185.0889083    185.0889088    189.8383220 
##    PZA01978.23     PZB00114.1     PZA02204.1    PZA00610.16     PZA03188.3 
##    191.6500379    191.6500384    192.7755002    195.5249503    196.4082510 
##     PZA02957.5     PZA03020.8     PZA00978.1     PZA01246.1     PZA02087.2 
##    198.9147055    199.5028446    199.5028451    199.7950403    199.7950408 
##    PZA00245.20    PHM18705.23     PZB01403.1     PZA00894.7     PZA03037.2 
##    199.7950413    201.6342822    201.9900286    203.2470124    203.5587641 
##   PZA03305.7.1     PZB01227.6     PZA02044.1    PZA00276.18    PZA00307.14 
##    203.8926074    203.8926079    220.2955401    227.1766648    227.1766653 
##     PZA00991.2     PZA00235.9     PZA00623.3    PZA02359.10      PHM9807.9 
##    227.1766658    228.3732152    230.0090612    231.1149891    231.1149896 
##   PZA01238.1.2     PZA01068.1    PZA00343.31     PHM1275.22     PZA00856.2 
##    231.4744031    231.4744036    231.9006103    232.9730657    232.9730662 
##    PZA00243.25     PHM7616.35     PZA00432.4     PZA01239.2     PZA01807.1 
##    232.9730667    236.4643858    236.4643863    236.4643868    236.4643873
(loglik.mds[c] <- attr(population_Z006.mds$geno[[c]]$map, "loglik"))
## [1] -2724.735

Grupo de ligação 2

c <- 2
mds.ord <- getMDSorder(cross = population_Z006.mds, chr = c)
## Stress: 0.74766
## Mean Nearest Neighbour Fit: 4.97056
population_Z006.mds <- switch.order(cross = population_Z006.mds, chr=c, order=mds.ord, maxit = 10000, tol=1e-5)
plotRF(population_Z006.mds, chr=c, col.scheme = "redblue")

pull.map(population_Z006.mds, chr = c)
##     PZA02769.1     PZA01060.1     PZA02480.1     PZA02390.1     PZA01140.1 
##      0.0000000      0.0000005      0.0000010      0.0000015     18.1981692 
##     PZA01680.3     PZA01259.1     PZA00836.1    PZA02015.11     PZA03167.5 
##     35.7900461     35.7900466     35.7900471     35.7900476     35.7900481 
##    PZA00545.26     PZA02068.1     PZA00980.1     PZA00963.3    PHM3512.186 
##     35.7900486     35.7900491     35.7900496     51.5700720     53.9956661 
##     PZA00395.2     PZA02667.1     PZB00765.1     PZA02060.1     PZA02513.1 
##     55.2262937     55.2262942     55.2262947     55.2262952     55.2262957 
##     PZA01265.1    PZA02820.17     PZA01142.4    PZA03024.16    PZA00652.17 
##     55.5351997     55.5352002     57.2550690     57.6004375     57.9448403 
##      PHM532.23     PZA02411.3     PZA03317.1     PZA03172.3     PZA01575.1 
##     61.7762813     61.7762818     61.7762823     61.7762828     61.7762833 
##     PZA02383.1     PZA03452.6      PHM5296.6     PZA02633.4     PZA03324.1 
##     61.7762838     61.7762843     61.7762848     61.7762853     61.7762858 
##     PZA02408.2    PHM1899.157     PZA01304.1     PZA02209.2     PZA02356.7 
##     61.7762863     61.7762868     61.7762873     61.7762878     61.7762883 
##     PZA03320.6     PZA02426.1     PZA02751.1    PZA00352.23     PZA03714.1 
##     61.7762888     61.7762893     61.7762898     61.7762903     62.5757105 
##     PZA03717.1     PZA01410.1     PZA00987.1     PZA02040.2    PZA00300.14 
##     62.5757110     66.0365026     66.4144766     66.4144771     66.4144776 
##    PZA00255.14     PZA02641.2   PZA01294.2.1     PZA03536.1     PZA01763.2 
##     67.8225561     67.8225566     67.8225571     67.8225576     68.4582310 
##        ae1.8.7     PZA02981.2     PZA00148.3     PZA01796.1     PZA01608.1 
##     68.4582315     68.4582320     71.0522987     72.8542427     73.5030441 
##     PZB01017.1    PZA00067.10     PZA01365.1    PZA02164.16     PZA00881.1 
##     73.5030446     75.6327253     76.7125500     79.7240474     79.7240479 
##    PZA00643.13    PZA03049.24     PZA01693.1     PZA00273.5     PZA01779.1 
##     79.7240484     81.0566420     82.3951081     82.3951086     84.0633272 
##     PZA02818.6     PZA02862.3     PZA00261.6     PZA01303.1     PHM5798.39 
##     84.4234442     84.4235197     86.0610758     86.0610763     86.9818596 
##     PZA03677.1     PZB01112.1     PZA02525.1     PZA01349.2     PZA01050.1 
##     86.9818601     86.9818606     86.9818611     86.9818616     86.9818621 
##     PHM4165.14     PZB00232.2      PHM3171.5     PZB01115.3     PZA02676.2 
##     86.9818626     88.1861214     88.5208458     88.5208463     89.1888556 
##     PZA03451.5     PHM1870.20     PZA00222.7     PZA01804.1     PZA00805.1 
##     90.2442897     90.2442902     90.2442907     90.2442912     90.2442917 
##  PZA00522.12.7     PHM3691.18     PZA01530.1     PZA00499.3     PZA00801.1 
##     90.2442922     90.2442927     90.2442932     90.2442937     90.2442942 
##     PZA00996.1     PHM12992.5      PHM4647.8     PZB00869.4     PZA02207.1 
##     90.2442947     90.2442952     90.2442957     90.2442962     90.2442967 
##     PZA00981.3     PHM16854.3     PZA01563.1     PZA02113.1     PZA00934.2 
##     90.2442972     90.2442977     90.2442982     98.4810592     98.4810597 
##      PHM565.31     PZA01427.1 PZA02792.26.25     PZA03274.4     PZA03226.3 
##     98.4810602     98.4810607    103.4035967    105.3393843    105.3393848 
##     PZA00517.7     PZA01523.1     PZA03578.1     PZA01327.1     PZA00985.1 
##    105.3393853    107.1707179    107.1707184    109.0971452    112.2322081 
##     PZA00112.5     PZA03092.7     PZA01284.6     PZB00079.4     PZA00865.1 
##    113.4066797    115.3383129    115.3383134    117.0039349    124.1989697 
##     PZA01371.1     PZA01925.1    PZA02029.21     PZB00094.1     PHM3137.17 
##    124.5634584    124.5634589    124.5634594    124.5634599    127.7957179 
##     PZA02753.1     PZB00054.3     PZA02462.1    PZA02653.12    PHM13122.43 
##    131.1684465    131.1684470    131.6043391    135.4225635    143.5416261 
##     PZA01570.1     PHM5359.10    PZA02316.22     PZA01438.1     PZA00191.5 
##    148.5527058    151.3497496    151.3497501    151.3497506    151.3497511 
##     PZA00818.1     PZA02367.1     PZA01983.1     PZA01887.1 
##    159.9400969    160.2288864    160.2288869    160.5080112
(loglik.mds[c] <- attr(population_Z006.mds$geno[[c]]$map, "loglik"))
## [1] -1662.614

Grupo de ligação 3

c <- 3
mds.ord <- getMDSorder(cross = population_Z006.mds, chr = c)
## Stress: 0.19014
## Mean Nearest Neighbour Fit: 3.79384
population_Z006.mds <- switch.order(cross = population_Z006.mds, chr=c, order=mds.ord, maxit = 10000, tol=1e-5)
plotRF(population_Z006.mds, chr=c, col.scheme = "redblue")

pull.map(population_Z006.mds, chr = c)
##     PZA00309.1     PZA02090.1     PZD00038.2     PZA02678.1    PZA00100.10 
##       0.000000       9.999378      18.740015      18.740016      19.066814 
##     PHM12859.7     PZA03527.1     PZA03212.3     PZA00749.1     PZB01944.1 
##      20.979107      20.979107      24.374767      24.717404      28.016456 
##     PZA02098.2     PZA01765.1     PZA00508.2     PHM4204.69     PHM4145.18 
##      28.016456      32.773358      32.773358      40.427878      49.699320 
##     PHM2343.25     PZA03054.5   PZA00210.1.9     PZA01473.1     PZA02427.1 
##      53.739350      53.739350      53.739351      53.739351      53.739352 
##    PZA00348.11     PZA02255.2         zb21.1     PZA00297.2    PZA00380.10 
##      53.739352      53.739353      53.739353      54.121361      54.121361 
##     PHM13823.7     PZA01114.2     PZA03070.9     PHM15899.9     PZA00581.3 
##      54.121362      54.121362      54.121363      54.121363      55.011455 
##     PHM15474.5     PZA03119.1     PZA00279.2     PZA01447.1     PZA02589.1 
##      55.011485      55.011485      55.011486      55.011486      55.448689 
##     PZA00509.1     PZA00265.6     PZA02699.1     PZA02296.1     PZA02742.1 
##      55.448689      55.448690      55.448690      56.593880      56.593880 
##     PHM5502.31     PZA02134.3     PZA02645.2     PZA00707.9     PZA02619.1 
##      56.593881      56.593881      56.593882      56.593882      57.537616 
##     PZA03198.3    PHM15449.10 PZA00413.20.18    PZA02299.16     PZA00363.7 
##      59.311944      60.085203      60.085203      60.782189      62.279624 
##     PZD00016.4     PZD00015.5     PZB02002.1     PHM1745.16     PZA02474.1 
##      62.279625      62.279625      62.279626      62.279626      64.910472 
##     PZA00920.1      PHM890.20     PZB02044.1     PZB02122.1     PHM4955.12 
##      65.337858      65.337865      65.337865      65.337866      65.995357 
##     PZA01934.6     PZA00827.1     PZA00948.1     PZA00828.2     PZB02179.1 
##      65.995358      66.744845      67.492249      68.042147      68.042148 
##     PHM9914.11     PZA00667.2     PZA01396.1     PHM4621.57     PZA00186.4 
##      68.042148      71.640208      72.881835      74.040436      74.040437 
##     PHM2885.31 PZA03073.28.26     PHM1959.26     PZD00027.2     PZA02402.1 
##      74.040437      75.203480      76.365095      76.365096      76.365096 
##    PZA03032.19     PZA00783.1     PZA01726.1     PZA02212.1    PZA01962.12 
##      77.413305      77.722749      79.688096      80.274608      87.429488 
##     PZA02654.3     PHM17210.5     PHM1675.29   PZA03191.1.4     PZA03733.1 
##      87.429488      87.429489      92.012925     100.054328     100.054329 
##     PZA03735.1         zb27.1     PZA00494.2     PZA03647.1     PZA01228.2 
##     100.054329     100.054330     102.183136     102.183137     102.183137 
##      PHM824.17     PZA03743.1     PZA03744.1    PHM13673.53     PZA03255.1 
##     102.183138     102.183138     102.183139     102.183139     103.478439 
##     PZB01109.1     PZA01035.1    PZA00308.24     PZA01457.1     PZA02122.9 
##     105.531260     105.531260     105.531261     105.531261     108.301748 
##     PZA01501.1     PZA00892.5     PZA03154.4     PZA02733.1 PZA00538.18.15 
##     108.635705     110.543503     113.087199     113.087200     119.514707 
##     PZA02516.1     PZA02616.1     PZB01457.1     PZA03146.4         sh2.21 
##     123.453325     125.496813     125.496813     132.905698     133.543260 
##     PZA00750.1     PZA01154.1     PHM3342.31     PZA02665.2     PZA01233.1 
##     133.803869     133.803869     138.890874     138.890874     138.890875 
##     PZA02514.1     PZA02824.4     PZA02668.2     PHM2672.19     PZA00219.7 
##     138.890875     138.890876     140.120492     141.994890     141.994891 
##     PZA03391.1     PZA00402.1    PZA00316.10     PZA01360.3     PZA01688.3 
##     141.994891     146.150859     153.092581     154.067433     154.067433 
##     PHM3852.23     PZA02182.1     PHM2423.33     PZA02423.1     PZA00088.3 
##     154.067434     155.078007     163.706763     166.154401     166.154401
(loglik.mds[c] <- attr(population_Z006.mds$geno[[c]]$map, "loglik"))
## [1] -1925.615

Grupo de ligação 4

c <- 4
mds.ord <- getMDSorder(cross = population_Z006.mds, chr = c)
## Stress: 0.78854
## Mean Nearest Neighbour Fit: 5.83293
population_Z006.mds <- switch.order(cross = population_Z006.mds, chr=c, order=mds.ord, maxit = 10000, tol=1e-5)
plotRF(population_Z006.mds, chr=c, col.scheme = "redblue")

pull.map(population_Z006.mds, chr = c)
##   PZA00365.2   PHM5817.15   PZA00680.3   PHM1511.14  PZA00525.17  PHM13440.13 
##    0.0000000    0.0000005    0.0000010    4.3269196    4.3269201    4.3269206 
##  PZA02133.10   PZA02681.8   PZA02175.1   PZA00902.1   PZA02264.5   PZB01233.1 
##    4.3269211    5.2273422    5.2273427    5.7002888    6.1729953    9.2529517 
##   PZA01211.1  PZA00172.12  PZA00613.22   PZA02208.1   PZA02081.1  PZA01935.10 
##   11.6054142   15.0162494   20.7955479   20.7955484   20.7955489   23.4537275 
##   PZA03699.1   PZA03747.1 PZB00901.3.4   PZA02272.3   PHM5822.15       zfl2.9 
##   25.4914113   25.4914118   32.5992946   33.3699461   34.9575027   42.1820901 
##   PZA00108.4   PZA01753.1   PZA02337.4   PZA03559.1   PZA02417.2   PZA00497.4 
##   46.3371240   46.3371245   46.9437298   46.9437303   46.9437308   57.3257135 
##   PZA03228.4   PZA03634.1    PHM6111.5   PZA00590.1   PZA01879.1   PZA03142.5 
##   58.2613263   59.2590234   60.7459197   60.7459202   61.1038256   62.9867134 
##   PHM1962.33   PZA02080.1   PZA01755.1   PZA03568.1   PZA01374.1   PHM4586.12 
##   66.2626294   66.2626299   66.2626304   66.2626309   67.8117715   69.2485990 
##   PZA01336.1   PZA02058.1   PZA01993.7   PZA02378.7   PZA02496.1   PHM10404.8 
##   69.2485995   70.1206689   71.2283521   71.2283526   71.2283531   71.2283536 
##   PZB00183.4   PZA02450.1   PZA01820.1   PZA02774.1   PZA03629.1   PZA02168.1 
##   74.4197451   76.5405275   76.5405280   77.6801163   79.7154619   79.7154624 
##   PZA02279.1   PZA00635.7    PHM3457.6  PHM10321.11   PZA01902.1  PHM4880.179 
##   83.7549941   83.7549946   83.7549951   86.2721952   86.2721957   86.2721962 
##  PHM13360.13   PZA02549.3   PZA00485.2    PHM3626.3  PZA00029.17   PZA02626.1 
##   86.2721967   86.2721972   86.2721977   89.7476103   89.7476108   89.7476113 
##   PZA03211.6   PZA01280.2   PZA01537.2  PZA02939.10   PZA01232.1   PZA01321.1 
##   89.7476118   90.3854580   91.0092256   91.3241212   91.3241217   92.7181651 
##   PZA02465.1   PZA02371.6  PZA00515.10   PZA00637.6     vdac1a.1   PZA03692.1 
##   92.7181656   93.7482804   93.7482809   93.7482814   93.7482819   93.7482824 
##   PZA01638.1   PZA03644.1   PZA00495.5   PZA03659.1   PZA00224.4   PZA03184.2 
##   93.7482829   93.7482834   93.7482839   96.0228644  100.8426031  100.8426036 
##   PZA00755.2   PZA01735.1   PZA03529.1   PZA02890.4    PHM3055.9   PHM3668.12 
##  100.8426041  100.8426046  109.1372945  115.0743026  115.0743031  115.0743036 
##   PZA00824.2   PZA02017.1   PHM7953.11   PZA00803.3  PHM16125.47   PZA02731.1 
##  115.0743041  115.0743046  115.0743051  115.0743949  115.3700171  115.3700176 
##   PZA02329.2   PZA03165.1   PZB01103.2   PHM14412.4   PZA00390.7   PZA01885.2 
##  115.3700181  115.3700186  118.7804634  118.7804639  118.7804644  118.7804649 
##   PZA02077.1   PZA02964.7   PZB00772.7   PZA03602.1   PZA02456.1   PZA00804.1 
##  118.7804654  118.7804659  118.7804664  123.8478957  124.9890404  126.1679016 
##   PZA02680.1   PZA02471.5   PZA02418.2  PZA00527.10   PZA02012.7   PZA02453.1 
##  127.0492138  127.9142884  127.9142889  127.9142894  137.4811510  137.4811515 
##   PZA00163.4   PZA01991.3   PZA02564.2   PZB01013.1   PZA02266.3   PZA01895.1 
##  140.5428911  140.8332408  140.8332413  141.7699165  144.6825217  145.3594199 
##   PZA01352.5   PZA02727.1   PZA02170.1   PHM3094.23   PZA03321.4   PZA03577.1 
##  146.7738498  147.1225370  161.6770781  164.8037277  164.8037282  166.2233389 
##   PZD00022.5 
##  167.3335640
(loglik.mds[c] <- attr(population_Z006.mds$geno[[c]]$map, "loglik"))
## [1] -1981.645

Grupo de ligação 5

c <- 5
mds.ord <- getMDSorder(cross = population_Z006.mds, chr = c)
## Stress: 0.6651
## Mean Nearest Neighbour Fit: 4.55648
population_Z006.mds <- switch.order(cross = population_Z006.mds, chr=c, order=mds.ord, maxit = 10000, tol=1e-5)
plotRF(population_Z006.mds, chr=c, col.scheme = "redblue")

pull.map(population_Z006.mds, chr = c)
##   PZA00282.19 PZA00682.17.2   PZA02239.12   PZA01905.12    PZA03598.1 
##     0.0000000     0.5994799     7.2727835     7.2727840     7.2727845 
##    PHM4125.11  PZA03322.5.3    PHM2100.21    PHM5665.26    PZA00529.4 
##    13.5211834    15.8407787    15.8407792    19.9391547    20.1690866 
##    PZA02585.2    PHM5599.20    PZA00513.1    PZA02151.3    PZA00694.6 
##    20.1690871    20.1690876    26.4480431    26.7354316    30.2365585 
##    PZA00521.3    PZA01367.2    PZA00878.2    PZA00636.7    PZA03155.3 
##    30.8930030    32.6881480    32.6881485    32.6881490    33.1876569 
##    PZA00155.1    PZA03081.1    PZB01021.1    PZA02479.1    PZA01332.2 
##    34.2096247    34.6334159    34.6334164    34.6334169    35.1237590 
##    PZA02779.1    PZA02614.2    PZA02421.1    PZA01566.1    PZA01810.2 
##    35.1237595    35.1237600    35.5304822    35.5304827    35.5304832 
##    PZA03205.1    PZA01790.1    PHM4348.16    PZA00193.2    PZA00332.5 
##    35.9372149    36.2880464    37.7575165    37.7575170    39.0431195 
##    PZB01461.1   PZA00344.10    PZA00941.2    PZA01954.1    PZA01766.1 
##    39.8788723    41.1535260    41.8258187    49.7968669    49.7968674 
##    PZA02289.2    PZA02194.1    PZA01976.9    PHM3637.14    PZD00030.2 
##    49.7968679    49.7968684    49.7968689    49.7968694    51.2061387 
##    PZA01187.1  PZA03275.4.1    PZA01477.3    PZA01658.1    PZA01681.1 
##    52.3035364    57.9138160    59.9283680    59.9283685    59.9283690 
##    PZA00271.1    PHM3155.14    PZA01289.1    PZA00453.2    PZA03116.1 
##    60.2742244    65.5196874    65.5196879    67.0811803    67.0811808 
##    PZA01926.1    PZA00057.2   PZA02992.15    PZA02982.7    PZA03152.3 
##    75.1112194    79.9139224    84.4330776    84.4330781    84.7292862 
##    PZA02147.1    PZA03459.1    PZA02027.1        fea2.3    PZA00704.1 
##    86.4918524    88.1100641    88.4562847    88.4562852    89.1574276 
##    PZA03409.1    PZB00093.7    PZA03231.1    PZA00104.1    PZA03203.2 
##    89.1574281    90.5669816    90.5669821    93.7990508    94.4642667 
##    PZA03564.1    PZA00218.1    PZA02767.1    PZA03270.2    PZA03597.1 
##    95.1297366    95.7952054    95.7952059    95.7952064    95.7952908 
##    PZA03254.1    PZA03587.1       bt2.7.4    PHM1307.11    PZA01759.1 
##    96.1119836    96.1119841    96.4283662    97.0677088    97.0677093 
##    PHM14055.6 PZA00726.8.10   PZA00445.22    PZA01751.2    PZA03385.1 
##    97.0677098    97.0677103    97.3840891    98.0560308    98.0560313 
##    PZA00541.1    PZA01106.3   PHM13623.14    PZA01713.4    PHM5572.19 
##    98.3759844    98.3759849    98.3759854    98.3759859    98.3759864 
##    PZA03247.1    PZA02705.1   PHM15427.11    PZA02457.1    PZA02002.1 
##    98.3759869   100.0722432   100.0722437   103.7507697   104.9354215 
##   PZA03048.18    PZA01422.3    PZA00139.4     PHM8527.2    PZA02385.6 
##   104.9354220   106.4426794   106.4426799   111.4208030   112.3608876 
##    PZA02138.1    PZA01122.1    PZA02358.1    PZA00975.1    PZA00683.4 
##   112.3608881   112.3608886   112.3608891   126.7622978   126.7622983 
##    PZA00436.7    PHM3301.28    PHM2438.28   PZA02509.15    PHM1184.26 
##   131.8018732   133.8214032   142.8475062   147.3096314   147.3096319 
##    PZA03227.1 
##   147.3096324
(loglik.mds[c] <- attr(population_Z006.mds$geno[[c]]$map, "loglik"))
## [1] -1750.665

Grupo de ligação 6

c <- 6
mds.ord <- getMDSorder(cross = population_Z006.mds, chr = c)
## Stress: 0.21975
## Mean Nearest Neighbour Fit: 3.33761
population_Z006.mds <- switch.order(cross = population_Z006.mds, chr=c, order=mds.ord, maxit = 10000, tol=1e-5)
plotRF(population_Z006.mds, chr=c, col.scheme = "redblue")

pull.map(population_Z006.mds, chr = c)
##    PHM5019.59    PZA02281.3    PZA00071.2    PZA01290.1    PHM2749.10 
##     0.0000000     0.0000005     0.0000010     0.0000015     0.0000020 
##    PZA00760.1   PHM14104.23   PZA00189.23    PHM14046.9     PHM4786.9 
##     2.6240710     2.6240715     7.7485206     8.9267685     8.9267690 
##   PZA01964.29    PZA00904.1    PZA01316.1    PZA00020.5    PZA00362.1 
##     8.9267695     8.9267700    25.8492504    26.1659543    28.3765769 
##  PZA00460.3.8    PZA02746.2    PHM1834.47    PZA00675.1   PHM15623.10 
##    30.1920471    34.1549581    34.1549586    34.8426863    39.4972170 
##   PZA00706.16    PZB00811.1     PHM3465.6    PZA00838.2    PZA01741.1 
##    39.4972175    39.4972180    39.4972185    39.4972190    44.9363785 
##   PHM12749.13    PZA03698.1    PHM15278.6    PZA00951.1    PZA03182.5 
##    45.6996382    45.6996387    45.6996392    45.6996397    45.6996402 
##    PHM4757.14    PZA03651.1    PZA03650.1    PZA00429.1    PZA02011.1 
##    47.1661283    47.1661288    47.1661293    50.3773044    52.6626249 
##    PZA00766.1     PHM448.23    PZA00770.1    PZA00090.1    PZA02033.1 
##    53.3777165    53.3777170    53.3777175    53.3777180    53.3777185 
##    PHM4203.11   PHM14152.18    PZA01787.1    PHM5468.25    PZA01049.1 
##    53.3777190    54.3557357    56.3201852    56.3201857    56.3201862 
##  PZA03612.2.1  PZA00118.1.5 PHM10525.9.11    PZA01038.1    PZB02155.1 
##    56.3201867    56.9970272    57.3028382    57.3028387    57.6082749 
##    PHM3993.28    PZA03638.1    PZA02748.3    PZA03637.1    PZA03639.1 
##    62.1849548    62.5513890    62.5513895    62.5513900    62.5513905 
##     PHM934.19    PZA03012.7    PZA01072.1    PZA02566.1   PZA01972.14 
##    62.5513910    63.0522399    63.0522404    63.0522409    64.6609793 
##    PZA03579.1    PZA00739.1     PHM4134.8    PZA02019.1   PHM3978.104 
##    66.8514287    66.8514292    66.8514297    68.8659793    69.1682653 
##    PZA00908.2    PZA03135.1    PZA02683.1    PZA02522.1    PZA01297.1 
##    69.1682658    69.1682663    69.1682668    71.9027921    71.9027926 
##    PZA01301.1    PZA01363.2    PZA01470.1    PHM11114.7    PZA01960.1 
##    71.9027931    71.9027936    74.1758928    74.4633663    74.4634563 
##    PZA00379.2   PZA00717.15    PZA01257.1    PZA00793.2    PZA02203.1 
##    74.4634568    74.4634573    74.4634578    74.4634583    75.6941118 
##    PZA01209.1    PZA00498.5    PZA01196.2    PHM2350.17    PZA01186.1 
##    75.6941123    75.6941128    75.6941133    75.6941138    77.7232014 
##    PHM6428.11    PZA00758.1   PHM1978.111    PZA02454.2    PZA02528.1 
##    77.7232019    77.7232024    79.7725240    86.2262013    86.5476551 
##    PZA01357.2    PZA01079.1    PZA02955.3    PZA01951.1     PHM9695.8 
##    89.6832284    91.6960181    91.6960186    95.7107287    96.7839618 
##    PZA01691.1    PZA03178.1    PZA01601.1    PZA00416.7    PHM4711.14 
##    98.2333668    98.9015073   108.9566295   109.2983248   111.1591943 
##    PZA00058.1    PZA00368.1    PZA01623.3    PZA01600.2    PZA02174.2 
##   116.4481390   116.4481395   116.4481400   121.1929348   135.6628449 
##    PZA02388.1 
##   135.6628454
(loglik.mds[c] <- attr(population_Z006.mds$geno[[c]]$map, "loglik"))
## [1] -1561.934

Grupo de ligação 7

c <- 7
mds.ord <- getMDSorder(cross = population_Z006.mds, chr = c)
## Stress: 0.19536
## Mean Nearest Neighbour Fit: 4.44413
population_Z006.mds <- switch.order(cross = population_Z006.mds, chr=c, order=mds.ord, maxit = 10000, tol=1e-5)
plotRF(population_Z006.mds, chr=c, col.scheme = "redblue")

pull.map(population_Z006.mds, chr = c)
##     PZA03573.1    PHM13681.12    PHM1911.173     PZA00708.3    PHM15445.25 
##       0.000000       9.469463       9.469464      17.470863      18.762702 
##     PZA00832.1     PHM4303.16     PZA00912.2     PZA02381.1     PZA00511.3 
##      18.762703      21.088766      25.212722      27.690309      32.636178 
##     PHM4604.18   PZA01715.1.2     PZA02197.1     PZB00221.3     PZD00055.1 
##      32.636178      32.636179      32.636236      32.636294      32.636351 
##     PZA00323.3    PHM11226.13      PHM816.29     PZA01369.1     PZA02252.2 
##      32.636409      33.082973      33.529753      34.435038      46.323007 
##      PHM1766.1     PZA01096.1    PZA02235.14     PZA03670.1     PZA03671.1 
##      49.607726      49.607727      54.737882      54.737883      54.737883 
##     PZA02111.1      PHM4905.6     PZA01866.1     PZA02397.1     PHM3330.25 
##      54.737884      54.737884      54.737885      54.737885      58.375704 
##    PZA00213.19     PZA00060.2     PZA00840.1     PZA01819.1     PZA02325.4 
##      58.712038      59.422722      59.422722      62.792914      62.792914 
##     PZA02613.1     PZA03235.1     PZA03470.1     PZA00015.5     PZB01358.1 
##      62.792915      62.792915      64.931892      65.435799      66.755215 
##     PZA00152.1     PZA00225.8     PZB01899.1     PZA00947.1     PZA01281.2 
##      66.755216      68.747441      73.288259      75.112118      75.112119 
##     PZA02545.1     PZA03596.1     PZA01062.1     PZA01861.1     PZB00761.1 
##      76.870159      76.870160      76.870160      77.502826      77.812020 
##     PZA01791.2     PZA00925.2     PZA03057.3    PZA00589.10     PZB00014.1 
##      78.436634      78.436634      78.436635      79.839317      79.839318 
##     PZA03469.1    PZA02878.13     PZA03036.6     PZA00693.3     PZB00959.1 
##      79.839318      79.839319      80.524318      80.885628      80.885658 
##     PZA02648.2     PZB01110.6     PZB00547.3     PZA01999.3          wx1.1 
##      81.247101      83.964592      83.964592      83.964593      83.964593 
##     PZB00544.2     PZB00540.3         ZHD1.1     PZB01042.2 PZA03058.22.21 
##      83.964594      83.964594      83.964676      84.220356      84.220357 
##          zb7.2     PZA00860.1      PHM9374.5     PZA03416.7     PZA02702.1 
##      84.220357      89.965996      90.600854      90.600854      92.236220 
##     PHM5181.10     PZA02344.1     PZA01386.3     PZA00466.1     PZA01195.3 
##      98.345805      98.345806     101.604657     101.604658     101.604658 
##      sh1.12.11      PHM1218.6   PZA01799.1.2     PZA00285.3     PHM3925.79 
##     101.604659     101.604659     104.605413     108.002301     118.013860
(loglik.mds[c] <- attr(population_Z006.mds$geno[[c]]$map, "loglik"))
## [1] -1400.083

Grupo de ligação 8

c <- 8
mds.ord <- getMDSorder(cross = population_Z006.mds, chr = c)
## Stress: 0.41463
## Mean Nearest Neighbour Fit: 4.36041
population_Z006.mds <- switch.order(cross = population_Z006.mds, chr=c, order=mds.ord, maxit = 10000, tol=1e-5)
plotRF(population_Z006.mds, chr=c, col.scheme = "redblue")

pull.map(population_Z006.mds, chr = c)
##   PZA02815.25    PZA01875.1    PHM4468.13    PZA00910.1    PZA02141.1 
##      0.000000      1.368224      1.368224      3.132220      9.139695 
##    PZB01222.1    PZA02688.2    PZA00821.1     PHM7922.8    PZA00889.2 
##     10.180003     10.505943     11.164875     13.692862     13.903537 
##    PZA01468.1    PZA00266.7    PHM4503.25    PZB01569.7    PHM4748.16 
##     25.704514     25.704515     25.704515     26.029379     34.060133 
##    PZA00223.4    PHM5794.13   PZA03027.12    PZA02436.1    PZA01462.1 
##     37.905432     37.905432     38.540580     38.540581     38.540581 
##    PZA01672.1    PZA01342.2    PZA01144.1   PHM11985.27    PZA02247.1 
##     38.540582     51.566572     52.507074     52.811855     54.519291 
##    PZA03102.9    PZB00942.1    PZB01308.1    PZA02148.1    PZA02673.1 
##     54.519292     54.861450     54.861450     54.861451     54.861451 
##  PZA02187.1.2    PZA02478.7    PZA02328.5    PZA02262.3   PZA00357.19 
##     54.861452     54.861452     56.291408     56.607849     56.607849 
##    PZA01884.1    PZB00414.2    PZA01552.1    PZA01591.1    PZA01618.2 
##     57.615569     58.267058     60.681044     60.681044     60.681045 
##    PZA00473.5    PZA01729.1   PHM13020.10    PZA00571.1        lac1.3 
##     60.681045     60.998440     60.998441     61.639650     61.639651 
##    PZA01055.1    PZA01736.1   PZA00382.17    PZA02048.2    PZA01589.2 
##     61.639651     61.955018     61.955018     63.990695     63.990695 
##   PZA02396.14    PZA01029.1    PZA03461.1    PZB01658.1    PZA00942.2 
##     63.990696     63.990696     70.273129     70.273129     78.853272 
##    PZA03488.1    PHM8909.12    PZA00214.1  PZB01009.2.1    PZD00072.2 
##     84.461261    100.921463    100.921463    100.921464    100.921464 
##    PHM2551.31   PZA00006.17    PZA02606.1  PZA03069.8.4   PZA00543.12 
##    100.921465    100.921465    102.464632    102.761761    103.044189 
##    PZA00427.3    PZA00355.2    PZA01425.2    PZA01527.1    PZA03120.1 
##    103.338498    103.338499    103.662940    106.589116    107.233615 
##    PZA01901.1    PZA01509.1   PZA03063.21 PZA00440.15.1   PZA02948.24 
##    107.233656    107.233656    107.573271    107.573272    107.573272 
##   PZA03047.12    PZA00158.2   PHM15961.13 
##    107.935170    110.033938    117.024573
(loglik.mds[c] <- attr(population_Z006.mds$geno[[c]]$map, "loglik"))
## [1] -1269.307

Grupo de ligação 9

c <- 9
mds.ord <- getMDSorder(cross = population_Z006.mds, chr = c)
## Stress: 0.22509
## Mean Nearest Neighbour Fit: 4.84618
population_Z006.mds <- switch.order(cross = population_Z006.mds, chr=c, order=mds.ord, maxit = 10000, tol=1e-5)
plotRF(population_Z006.mds, chr=c, col.scheme = "redblue")

pull.map(population_Z006.mds, chr = c)
##   PZA01044.1   PZA02274.1   PZA00424.1   PZA01744.1   PZA01278.2   PZB00605.1 
##    0.0000000    0.0000005    0.0000010    0.0000015   15.9314176   15.9314181 
##   PZA00695.3   PZA00386.4   PZA01028.2   PZA01414.1   PZA01802.3   PZA00505.6 
##   16.5841595   19.7583385   19.7583390   21.5401174   22.2699384   23.1340657 
##   PZA01533.2  PHM10225.15   PZA02223.2   PHM7898.10   PZA02373.1   PZA00795.1 
##   23.1340662   24.4540164   25.0926366   25.0926371   25.0926376   29.2611598 
##   PZA02386.2   PZA02260.2     PHM112.8   PZA03176.4   PZA02722.1  PHM16437.20 
##   43.0337909   43.0337914   43.0337919   46.4076417   52.3469293   52.9991366 
## PZA00405.7.6  PHM9162.135  PZA02854.13   PZA03166.1   PZA03728.1  PZA02984.10 
##   57.0887284   57.3881593   58.0795623   58.0795628   58.0798661   59.2181086 
##  PZA02449.13  PZA00111.10   PZA00740.3   PZD00054.1   PZA01542.1   PZB00752.1 
##   59.5712748   59.9245193   59.9245198   59.9245203   59.9245208   60.5932522 
##   PZA01714.1   PZA03583.1   PZA02643.1   PZA02352.1   PZA00986.1   PZA01946.7 
##   60.5932527   60.5932532   62.6804039   69.0446418   70.4919154   70.7988363 
##   PZA01690.7  PZA00616.13   PZA02365.7   PZA02236.1   PZA01933.3   PZA01113.1 
##   70.7989219   71.0214203   72.8710172   82.0578529   83.9594008   83.9594013 
##   PZA02018.1 PZA01210.1.2   PZA01607.1   PZA01445.1   PZA00418.2   PHM4818.15 
##   83.9594018   84.8169833   84.8169838   84.8169843   84.8169848   84.8169853 
##  PHM12830.14   PZA02291.1    PHM904.21   PZA03645.1   PZA02612.1   PZA03723.1 
##   84.8169858   84.8169863   84.8169868   84.8169873   85.1702017   85.1702022 
##   PZA01936.4   PZA03363.1   PZA01230.1   PZA00084.2   PZA03687.1   PHM15501.9 
##   85.1702027   85.1702032   85.1702037   85.1702042   85.8640689   86.1639262 
##  PZA00132.17   PHM4353.31   PZA03344.2   PHM4080.15  PZA00256.27   PZA03624.1 
##   86.1639267   86.1639272   88.4751208   89.8497802   89.8497807   89.8497812 
##   PZA02872.1 PZA01909.1.2   PHM3078.12   PHM9241.13   PZA02035.5   PZA01426.1 
##   95.0834481  111.4033230  116.1538908  116.1538913  129.4943904  135.5417034
(loglik.mds[c] <- attr(population_Z006.mds$geno[[c]]$map, "loglik"))
## [1] -1390.431

Grupo de ligação 10

c <- 10
mds.ord <- getMDSorder(cross = population_Z006.mds, chr = c)
## Stress: 0.15726
## Mean Nearest Neighbour Fit: 3.37458
population_Z006.mds <- switch.order(cross = population_Z006.mds, chr=c, order=mds.ord, maxit = 10000, tol=1e-5)
plotRF(population_Z006.mds, chr=c, col.scheme = "redblue")

pull.map(population_Z006.mds, chr = c)
##   PZA02527.2   PZA00062.4   PZA02578.1   PZA02167.2   PZA01001.2   PZA01073.1 
##      0.00000     13.05977     15.74208     15.74208     15.74208     25.36570 
##   PHM5435.25   PZA00007.1   PZA02049.1   PZA02969.9   PZA00130.9   PZA03607.1 
##     26.53406     26.53407     26.92644     27.54907     27.80973     33.42354 
##   PZA03604.1   PZA03606.1   PZA03605.1   PZA03603.1   PZA01995.2   PZA02663.1 
##     33.42354     33.42354     33.42354     33.42354     36.35372     39.60807 
##  PHM15868.56 PHM18513.156   PZA01456.2   PZB01111.8   PZA02320.1   PZA01241.2 
##     39.60807     39.60807     41.69741     45.26014     48.29973     50.20221 
##   PZA00647.9   PZA01005.1   PZA00866.2   PZA03196.1   PZA03713.1   PZA01141.1 
##     51.77917     56.28237     56.28237     56.63489     56.63489     58.23680 
##  PHM13687.14   PZA02219.2   PZA01089.1   PHM4341.42   PZA02128.3   PZA01919.2 
##     59.03320     59.03320     59.82983     59.82983     61.42730     61.74886 
##   PZA00444.1  PHM12625.18   PHM18195.6   PZA01292.1   PZA02398.2    PHM537.22 
##     61.74886     61.74886     61.74886     61.74886     61.74894     61.74900 
##   PZA00400.3   PZA00048.1   PZA01619.1  PHM12990.15   PZA00814.1   PZA00337.4 
##     62.08440     63.00017     63.00017     63.92807     63.92807     64.27397 
##   PZB00409.6   PZA01877.2   PZA02941.7   PHM2770.19   PZA00933.3   PZA01677.1 
##     64.76102     65.75641     66.91391     66.91391     66.91391     66.91391 
##   PZA03491.1   PZA01597.1  PZA00409.17   PZD00033.3  PZA02853.11   PZA02470.2 
##     67.67358     67.67358     67.67358     68.04093     68.04093     68.04093 
##   PZA00079.1   PHM3922.32   PZA02961.6   PZA01642.1    PHM3896.9   PZA00463.3 
##     71.16624     71.16624     71.89335     73.31298     73.31298     74.02590 
##  PHM15331.16   PZB01301.5   PZA01451.1   PZA01883.2  PZA02095.10    PHM3765.7 
##     77.92549     79.54458     81.08033     81.08033     81.08033     81.08033 
##   PHM3631.47   PHM2828.83   PZA01313.2  PZA02221.20   PZA02554.1 
##     81.08033     81.08033     81.08033    114.80429    115.35936
(loglik.mds[c] <- attr(population_Z006.mds$geno[[c]]$map, "loglik"))
## [1] -1184.876

Ajustes manuais da orderMarkers

knitr::kable(cbind(summaryMap(population_Z006.mds), log.likelihood=c(loglik.mds, sum(loglik.mds))))
n.mar length ave.spacing max.spacing log.likelihood
1 175 236.4644 1.358991 16.402932 -2724.735
2 139 160.5080 1.163102 18.198168 -1662.614
3 130 166.1544 1.288019 9.999378 -1925.615
4 127 167.3336 1.328044 14.554541 -1981.645
5 111 147.3096 1.339179 14.401409 -1750.665
6 106 135.6628 1.292027 16.922480 -1561.934
7 85 118.0139 1.404927 11.887969 -1400.083
8 78 117.0246 1.519800 16.460201 -1269.307
9 78 135.5417 1.760282 16.319875 -1390.431
10 77 115.3594 1.517886 33.723956 -1184.876
overall 1106 1499.3723 1.368040 33.723956 -16851.905
plotMap(population_Z006.mds)

plotRF(population_Z006.mds, col.scheme = "redblue")

save.image("population_Z006_mds.RData") 

Mapeamento QTL

Aqui, estamos usando a linha de raça recombinante (RIL) população Z006, de modo que esses métodos podem não levar aos mesmos resultados em sua população.

library(qtl)
load("population_Z006_mds.RData")
summaryMap(population_Z006.mds)
##         n.mar length ave.spacing max.spacing
## 1         175  236.5         1.4        16.4
## 2         139  160.5         1.2        18.2
## 3         130  166.2         1.3        10.0
## 4         127  167.3         1.3        14.6
## 5         111  147.3         1.3        14.4
## 6         106  135.7         1.3        16.9
## 7          85  118.0         1.4        11.9
## 8          78  117.0         1.5        16.5
## 9          78  135.5         1.8        16.3
## 10         77  115.4         1.5        33.7
## overall  1106 1499.4         1.4        33.7
plot.map(population_Z006.mds)

plotRF(population_Z006.mds, col.scheme = "redblue")

Análise de marcadores únicos

Nossa primeira análise envolve testes para associações entre cada marcador e o traço “PlantHeight”. Fazemos isso executando a função com , que significa “regressão de marcador”:scanone()method = “mr”

population_Z006.mr <- scanone(population_Z006.mds, pheno.col = "PlantHeight", method = "mr")

Existem funções chamadas e que podem ser aplicadas a qualquer objeto de mapeamento QTL. A função combinada com o argumento mostra a estatística de teste (pontuação LOD) para cada marcador como pontos.plot()summary()plot()type = “p”

plot(population_Z006.mr, type = "p", main = "Análise de Marcador Simples")

summary(population_Z006.mr)
##            chr   pos   lod
## PZA03551.1   1  24.8 2.992
## PZA03092.7   2 115.3 0.677
## PZA01765.1   3  32.8 3.823
## PHM3055.9    4 115.1 3.742
## PZA03155.3   5  33.2 2.144
## PHM3993.28   6  62.2 0.535
## PZA01791.2   7  78.4 5.097
## PZA00158.2   8 110.0 0.869
## PZA03687.1   9  85.9 2.283
## PZD00033.3  10  68.0 5.246
population_Z006.perm.mr <- scanone(cross = population_Z006.mds, pheno.col = "PlantHeight", method = "mr",
    n.perm = 1000, verbose = FALSE)

O resumo da função mostra os marcadores com a maior pontuação lod para cada cromossomo:

plot(population_Z006.perm.mr)

Testes de permutação podem fornecer um valor crítico que pode ser usado para declarar QTL. No nosso caso, usamos , o que significa que estamos executando 1.000 permutações:n.perm = 1000

A função mostra o limiar LOD para um determinado summary()

summary(population_Z006.perm.mr, alpha = 0.05)
## LOD thresholds (1000 permutations)
##     lod
## 5% 3.08

Finalmente, podemos aplicar esse limite e ver quantos QTL temos baseado no SMA:

population_Z006.mr.sig <- summary(population_Z006.mr, perms = population_Z006.perm.mr, alpha = 0.05)
knitr::kable(population_Z006.mr.sig)
chr pos lod
PZA01765.1 3 32.77336 3.822740
PZA02890.4 4 115.07430 3.741960
PZA01791.2 7 78.43663 5.097006
PZD00033.3 10 68.04093 5.245950

A tabela apresentam os maiores valores de LOD para cada um dos grupos de ligação. Há uma correspondência entre as posições apontadas pelos dois métodos, se nos intervalos a posição não foi a mesma entre os métodos, é possível verificar que são bem aproximadas.

Mapeamento de intervalo

Para executar o IM, precisamos calcular a probabilidade de genótipo QTL dentro de intervalos de marcadores, condicionadas no mapa genético. Fazemos isso usando a função. O argumento define o tamanho da etapa em que a probabilidade precisa ser calculada (no nosso caso, a cada 1 cM):calc.genoprob()step = 1

population_Z006.mds <- calc.genoprob(cross = population_Z006.mds, step = 1)

A função para executar O mapeamento de intervalo é - a mesma para análise de marcadores únicos -, mas precisamos definir o método de estimativa como (máxima probabilidade via algoritmo de Maximização de Expectativa) ou (menos quadrados via regressão Haley-Knott). Podemos correr porque é mais rápido e geralmente funciona bem quando comparado com :scanone () em hk method = “hk” em

population_Z006.im <- scanone(cross = population_Z006.mds, pheno.col = "PlantHeight", method = "hk")
summary(population_Z006.im)
##            chr   pos   lod
## PZA03551.1   1  24.8 2.806
## PZA03092.7   2 115.3 0.597
## c3.loc35     3  35.0 3.895
## c4.loc112    4 112.0 3.292
## PZA03155.3   5  33.2 1.996
## PHM3993.28   6  62.2 0.489
## c7.loc79     7  79.0 5.163
## PZA00158.2   8 110.0 0.773
## PZA03687.1   9  85.9 2.523
## PZA03491.1  10  67.7 5.289

Para saber o valor crítico para declarar QTL usando permutações, primeiro executamos a função com os argumentos e:scanone () method = “hk” n.perm = 1000

population_Z006.perm.im <- scanone(cross = population_Z006.mds, pheno.col = "PlantHeight", method = "hk",
    n.perm = 1000, verbose = FALSE)

Em seguida, a função mostra o limiar LOD para um determinado summary() αα nível (no nosso caso,):alpha = 0.05

summary(population_Z006.perm.im, alpha = 0.05)
## LOD thresholds (1000 permutations)
##    lod
## 5% 3.1
plot(population_Z006.im, col = "red", main = "Mapeamento de Intervalo")
add.threshold(population_Z006.im, perms = population_Z006.perm.im, alpha = 0.05, col = "red")

Um enredo para comparar duas abordagens:

plot(population_Z006.mr, population_Z006.im, type = c("p", "l"), col = c("black", "red"), main = "Análise de Marcador Simples vs Mapeamento de Intervalo")
add.threshold(population_Z006.mr, perms = population_Z006.perm.mr, alpha = 0.05, lty = 2, col = "black")
add.threshold(population_Z006.im, perms = population_Z006.perm.im, alpha = 0.05, col = "red")
legend("topright", legend = c("AMS", "MP"), lty = c(2, 1), col = c("black", "red"))

Agora, podemos aplicar o limiar ao nosso objeto mapeamento de Intervalo, que dá uma lista das regiões QTL que são altamente significativas:

population_Z006.im.sig <- summary(population_Z006.im, perms = population_Z006.perm.im, alpha = 0.05)
population_Z006.im.sig
##            chr   pos  lod
## c3.loc35     3  35.0 3.89
## c4.loc112    4 112.0 3.29
## c7.loc79     7  79.0 5.16
## PZA03491.1  10  67.7 5.29

Observe que podemos usar para extrair os cromossomos () e posições () de nossa QTL significativa: population_Z006.im.sig. sig chr pos

chr <- population_Z006.im.sig$chr
pos <- population_Z006.im.sig$pos

Finalmente, o e são usados para mostrar as estimativas de efeito QTL para uma população RIL com base em um objeto derivado da função: makeqtl() fitqtl() sim.geno()

population_Z006.mds <- sim.geno(cross = population_Z006.mds, step = 1)
population_Z006.im.qtl <- makeqtl(cross = population_Z006.mds, chr = chr, pos = pos)
population_Z006.im.qtl
##   QTL object containing imputed genotypes, with 16 imputations. 
## 
##       name chr     pos n.gen
## Q1  3@35.0   3  35.000     2
## Q2 4@112.0   4 112.000     2
## Q3  7@79.0   7  79.000     2
## Q4 10@67.7  10  67.674     2

Se quisermos saber o efeito da QTL, e usarmos a função com o argumento, e, respectivamente:Q1 Q2 Q3 fitqtl() formula = y ~ Q1 formula = y ~ Q2 formula = y ~ Q3

population_Z006.im.fit.Q1 <- fitqtl(cross = population_Z006.mds, pheno.col = "PlantHeight", qtl = population_Z006.im.qtl,
    formula = y ~ Q1, get.ests = TRUE)
summary(population_Z006.im.fit.Q1)
## 
##      fitqtl summary
## 
## Method: multiple imputation 
## Model:  normal phenotype
## Number of observations : 185 
## 
## Full model result
## ----------------------------------  
## Model formula: y ~ Q1 
## 
##        df        SS        MS      LOD     %var Pvalue(Chi2)    Pvalue(F)
## Model   1  3382.399 3382.3986 3.190665 7.635236 0.0001264781 0.0001405089
## Error 183 40917.458  223.5927                                            
## Total 184 44299.857                                                      
## 
## 
## Estimated effects:
## -----------------
##               est      SE       t
## Intercept 147.622   1.103 133.822
## 3@35.0     -4.338   1.117  -3.886
population_Z006.im.fit.Q2 <- fitqtl(cross = population_Z006.mds, pheno.col = "PlantHeight", qtl = population_Z006.im.qtl,
    formula = y ~ Q2, get.ests = TRUE)
summary(population_Z006.im.fit.Q2)
## 
##      fitqtl summary
## 
## Method: multiple imputation 
## Model:  normal phenotype
## Number of observations : 185 
## 
## Full model result
## ----------------------------------  
## Model formula: y ~ Q2 
## 
##        df        SS        MS      LOD     %var Pvalue(Chi2)    Pvalue(F)
## Model   1  3432.016 3432.0157 3.239408 7.747239  0.000112285 0.0001249311
## Error 183 40867.841  223.3215                                            
## Total 184 44299.857                                                      
## 
## 
## Estimated effects:
## -----------------
##               est      SE       t
## Intercept 147.921   1.105 133.918
## 4@112.0    -4.352   1.112  -3.914
population_Z006.im.fit.Q3 <- fitqtl(cross = population_Z006.mds, pheno.col = "PlantHeight", qtl = population_Z006.im.qtl,
    formula = y ~ Q3, get.ests = TRUE)
summary(population_Z006.im.fit.Q3)
## 
##      fitqtl summary
## 
## Method: multiple imputation 
## Model:  normal phenotype
## Number of observations : 185 
## 
## Full model result
## ----------------------------------  
## Model formula: y ~ Q3 
## 
##        df        SS        MS     LOD     %var Pvalue(Chi2)   Pvalue(F)
## Model   1  5342.667 5342.6671 5.16286 12.06024  1.08232e-06 1.27848e-06
## Error 183 38957.189  212.8808                                          
## Total 184 44299.857                                                    
## 
## 
## Estimated effects:
## -----------------
##               est      SE       t
## Intercept 147.481   1.080 136.568
## 7@79.0      5.442   1.086   5.011
population_Z006.im.fit.Q4 <- fitqtl(cross = population_Z006.mds, pheno.col = "PlantHeight", qtl = population_Z006.im.qtl,
    formula = y ~ Q4, get.ests = TRUE)
summary(population_Z006.im.fit.Q4)
## 
##      fitqtl summary
## 
## Method: multiple imputation 
## Model:  normal phenotype
## Number of observations : 185 
## 
## Full model result
## ----------------------------------  
## Model formula: y ~ Q4 
## 
##        df        SS       MS      LOD     %var Pvalue(Chi2)    Pvalue(F)
## Model   1  5124.498 5124.498 4.938514 11.56775 1.852068e-06 2.172548e-06
## Error 183 39175.359  214.073                                            
## Total 184 44299.857                                                     
## 
## 
## Estimated effects:
## -----------------
##               est      SE       t
## Intercept 147.598   1.082 136.355
## 10@67.7     5.273   1.078   4.892

Estas seriam a expressão exata do modelo de QTL único como o método mapeamento de intervalo é. Ou seja, valores não podem ser somados. Foram estimados de forma independente e, portanto, não contabilizam a covariância entre os efeitos.

Uma maneira melhor de saber a quantidade de variação explicada pelos três QTL é construindo um modelo QTL múltiplo:

population_Z006.im.fit <- fitqtl(cross = population_Z006.mds, pheno.col = "PlantHeight", qtl = population_Z006.im.qtl,
    formula = y ~ Q1 + Q2 + Q3 + Q4, get.ests = TRUE)
summary(population_Z006.im.fit)
## 
##      fitqtl summary
## 
## Method: multiple imputation 
## Model:  normal phenotype
## Number of observations : 185 
## 
## Full model result
## ----------------------------------  
## Model formula: y ~ Q1 + Q2 + Q3 + Q4 
## 
##        df       SS        MS      LOD     %var Pvalue(Chi2) Pvalue(F)
## Model   4 16611.92 4152.9804 18.88034 37.49882            0         0
## Error 180 27687.94  153.8219                                         
## Total 184 44299.86                                                   
## 
## 
## Drop one QTL at a time ANOVA table: 
## ----------------------------------  
##         df Type III SS   LOD   %var F value Pvalue(Chi2) Pvalue(F)    
## 3@35.0   1        2218 3.095  5.006   14.42            0     2e-04 ***
## 4@112.0  1        3595 4.904  8.115   23.37            0  2.85e-06 ***
## 7@79.0   1        5592 7.389 12.622   36.35            0  9.12e-09 ***
## 10@67.7  1        4916 6.565 11.096   31.96            0  6.09e-08 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Estimated effects:
## -----------------
##                est       SE       t
## Intercept 147.4300   0.9117 161.713
## 3@35.0     -3.8401   0.9930  -3.867
## 4@112.0    -4.6228   0.9497  -4.868
## 7@79.0      5.5605   0.9572   5.809
## 10@67.7     4.9275   0.9678   5.092
save.image("population_Z006_im.RData")

Mapeamento de intervalos compostos

Para executar CIM, existe uma função chamada . Nesta função, precisamos especificar dois argumentos principais: cim()

O número de cofatores ou marcadores covariados: , que podem ser dados como n.marcovar 2×N−−√≈272×N−−√≈27. O tamanho da janela: , que podem ser valores diferentes dependendo do tamanho da população e saturação do mapa. Geralmente vai de 5 a 15 cM, mas pode ir até 20 ou 30 cM, ou tomar todo o cromossomo (ou seja, deixar um cromossomo fora - LOCO). window

population_Z006.cim10 <- cim(cross = population_Z006.mds, pheno.col = "PlantHeight", method = "hk", n.marcovar = 2 *
    sqrt(nind(population_Z006)), window = 10)
population_Z006.cim15 <- cim(cross = population_Z006.mds, pheno.col = "PlantHeight", method = "hk", n.marcovar = 2 *
    sqrt(nind(population_Z006)), window = 15)
population_Z006.cim20 <- cim(cross = population_Z006.mds, pheno.col = "PlantHeight", method = "hk", n.marcovar = 2 *
    sqrt(nind(population_Z006)), window = 20)
population_Z006.cimInf <- cim(cross = population_Z006.mds, pheno.col = "PlantHeight", method = "hk",
    n.marcovar = 2 * sqrt(nind(population_Z006.mds)), window = Inf)
summary(population_Z006.cim10)
##             chr   pos    lod
## c1.loc193     1 193.0  7.162
## PZA01303.1    2  86.1  0.651
## PZA01934.6    3  66.0  6.422
## c4.loc113     4 113.0 11.411
## PZA03048.18   5 104.9  5.628
## PZA02388.1    6 135.7  3.175
## PZA02878.13   7  79.8 14.375
## c8.loc80      8  80.0  7.015
## c9.loc78      9  78.0  6.554
## PZA00647.9   10  51.8  4.168
summary(population_Z006.cim15)
##            chr   pos   lod
## c1.loc155    1 155.0  7.81
## PZA01284.6   2 115.3  1.76
## PZA00309.1   3   0.0  5.16
## c4.loc113    4 113.0  6.94
## PZA02479.1   5  34.6  7.59
## c6.loc132    6 132.0  4.53
## PZB01042.2   7  84.2 11.75
## c8.loc81     8  81.0  1.04
## c9.loc79     9  79.0  4.17
## c10.loc67   10  67.0  8.19
summary(population_Z006.cim20)
##               chr   pos   lod
## c1.loc21        1  21.0  4.93
## PZA03092.7      2 115.3  3.29
## c3.loc63        3  63.0  8.68
## c4.loc113       4 113.0 13.51
## PZA00155.1      5  34.2  5.80
## PHM10525.9.11   6  57.3  3.49
## PZB00761.1      7  77.8 16.64
## c8.loc80        8  80.0  4.64
## PZA03687.1      9  85.9  4.15
## PZD00033.3     10  68.0 10.79
summary(population_Z006.cimInf)
##             chr   pos   lod
## PZA03551.1    1  24.8  6.46
## PZA01265.1    2  55.5  1.20
## c3.loc70      3  70.0  9.60
## c4.loc113     4 113.0  9.46
## c5.loc25      5  25.0  3.59
## c6.loc132     6 132.0  7.30
## PZB00761.1    7  77.8 14.84
## c8.loc77      8  77.0  4.08
## c9.loc77      9  77.0  6.62
## PHM12990.15  10  63.9 13.28

A fim de fazer os testes de permutação serem menos conservadores e fazê-los funcionar para qualquer tamanho de janela, podemos escolher . Isso significa que não serão permitidos cofatores com o grupo de vinculação onde os testes estão sendo realizados: window = Inf

set.seed(8617)
population_Z006.perm.cim <- cim(cross = population_Z006.mds, pheno.col = "PlantHeight", method = "hk",
    n.marcovar = 2 * sqrt(nind(population_Z006.mds)), window = Inf, n.perm = 1000)

Você pode usar mais threads de computador se acontecer de você ter um computador com vários núcleos usando o argumento (por exemplo), para que os testes de permutaion sejam executados mais rápido. n.clustern. cluster = 4

summary(population_Z006.perm.cim, alpha = 0.05)
## LOD thresholds (1000 permutations)
##    [,1]
## 5% 7.29

Agora, se usarmos esses resultados de permutação para identificar o QTL mais significativo, temos:

summary(population_Z006.cim10, perms = population_Z006.perm.cim, alpha = 0.05)
##             chr   pos  lod
## c4.loc113     4 113.0 11.4
## PZA02878.13   7  79.8 14.4
summary(population_Z006.cim15, perms = population_Z006.perm.cim, alpha = 0.05)
##            chr   pos   lod
## c1.loc155    1 155.0  7.81
## PZA02479.1   5  34.6  7.59
## PZB01042.2   7  84.2 11.75
## c10.loc67   10  67.0  8.19
summary(population_Z006.cim20, perms = population_Z006.perm.cim, alpha = 0.05)
##            chr   pos   lod
## c3.loc63     3  63.0  8.68
## c4.loc113    4 113.0 13.51
## PZB00761.1   7  77.8 16.64
## PZD00033.3  10  68.0 10.79
summary(population_Z006.cimInf, perms = population_Z006.perm.cim, alpha = 0.05)
##             chr   pos   lod
## c3.loc70      3  70.0  9.60
## c4.loc113     4 113.0  9.46
## c6.loc132     6 132.0  7.30
## PZB00761.1    7  77.8 14.84
## PHM12990.15  10  63.9 13.28
plot(population_Z006.cim10, population_Z006.cim15, population_Z006.cimInf, col = c("blue", "orange", "cyan"), main = "Mapeamento de Intervalo Composto")
add.threshold(population_Z006.cim10, perms = population_Z006.perm.cim, alpha = 0.05)
legend("topright", legend = c("ws = 10", "ws = 15", "ws = Inf"), lty = 1, col = c("blue",
    "orange", "cyan"))

Porque não declaramos a maior parte da QTL que achamos que são verdadeiras, e tem os mesmos resultados que, nós os retiramos da trama. Podemos comparar nossos resultados de CIM quando e com nossos resultados de mapeamento de intervalo: population_Z006cim20 population_Z006.cim15 population_Z006.cimInf. window = Inf

plot(population_Z006.im, population_Z006.cim10, population_Z006.cimInf, col = c("red", "blue", "cyan"), main = "Mapeamento de Intervalo vs Mapeamento de Intervalo Composto")
add.threshold(population_Z006.cim10, perms = population_Z006.perm.cim, alpha = 0.05)
add.threshold(population_Z006.im, perms = population_Z006.perm.im, alpha = 0.05, col = "red")
legend("topright", legend = c("MI", "MIC (ws = 10)", "MIC (ws = Inf)"), lty = 1,
    col = c("red", "blue", "cyan"))

Também podemos limitar a visualização a cromossomos onde qtl apareceu nas análises mapeamento de intervalo vs mapeamento de intervalo composto:

plot(population_Z006.im, population_Z006.cim10, population_Z006.cimInf, col = c("red", "blue", "cyan"), chr = c(1, 3, 4, 7, 10), main = "Mapeamento de Intervalo vs Mapeamento de Intervalo Composto")
add.threshold(population_Z006.im, perms = population_Z006.perm.im, alpha = 0.05, col = "red")
add.threshold(population_Z006.cim10, perms = population_Z006.perm.cim, alpha = 0.05, col = "black")
add.cim.covar(population_Z006.cimInf, chr = c(1, 3, 4, 7, 10), col = "green")
legend("topleft", legend = c("MI", "MIC (ws = 10)", "MIC (ws = Inf)"), lty = 1, col = c("red",
    "blue", "cyan"))

Os pontos verdes representam a localização dos marcadores selecionados como covariados (cofatores) para a pesquisa do mapeamento de intervalo composto com.window = Inf

Notei que o QTL no cromossomo 1 e 3 que identificamos usando mapeamento de intervalo está logo abaixo do limiar para a análise do mapeamento de intervalo composto - por isso os chamamos de QTL suggetive aqui. Dependendo da decisão do pesquisador, poderíamos abaixar um pouco o limiar para incluí-lo como um QTL usando mapeamento de intervalo composto, o que provavelmente está bem. Veremos, no entanto, uma maneira melhor de lidar com isso ao executar um modelo de QTL múltiplo, que é o nosso próximo tópico.

Neste momento, suponhamos que queremos investigar as estimativas de QTL sob mapeamento de intervalo composto. Só precisamos armazenar os cromossomos QTL e posições do nosso modelo selecionado (mapeamento de intervalo composto com window = 10ws=10ws=10):

set.seed(8617)
population_Z006.cim.sig <- summary(population_Z006.cim20, perms = population_Z006.perm.cim, alpha = 0.05)
population_Z006.cim.sig

No entanto, nottei que apenas um QTL no cromossomo 7 está listado (aquele com maior pontuação lod). Precisamos encontrar o outro olhando para as posições naquele cromossomo que tem uma pontuação LOD maior que o nosso limiar de 7.29:

peak.markers <- which(population_Z006.cim10$lod[population_Z006.cim10$chr == 7] > 7.29)
population_Z006.cim10$lod[peak.markers]
##  [1] 0.3737050 0.4410512 0.4982776 0.5334941 0.5411826 0.4975599 0.4877348
##  [8] 0.3925818 0.3925818 0.3925819 0.4570803 0.4778816 0.4719463 0.4684601
## [15] 0.4220755 0.5212033 0.5301639 0.6080766 0.6976733 0.7869117 0.8565369
## [22] 0.8809354 1.4714431 1.5432509 1.5432509 1.6461306 2.4933477 3.2083408
## [29] 3.2420422 4.7817944 5.3837805 5.4329987 5.1992741 4.3416781 0.5180057
population_Z006.cim10$pos[peak.markers]
##  [1] 68.00000 69.00000 70.00000 71.00000 71.68443 72.00000 72.05388 72.39298
##  [9] 72.39298 72.39298 73.00000 73.35819 73.67337 74.00000 74.68083 75.00000
## [17] 75.03281 76.00000 77.00000 78.00000 79.00000 80.00000 81.00000 81.87065
## [25] 81.87065 82.00000 83.00000 83.97939 84.00000 85.00000 85.58327 86.00000
## [33] 87.00000 88.00000 88.59222

Para mostrar as estimativas de efeito QTL para uma população RIL, precisamos usarmos a função, então e: sim.geno() makeqtl() fitqtl()

population_Z006.mds <- sim.geno(population_Z006.mds, step = 1)
population_Z006.cim.qtl <- makeqtl(cross = population_Z006.mds, chr = chr, pos = pos)
population_Z006.cim.qtl
##   QTL object containing imputed genotypes, with 16 imputations. 
## 
##       name chr     pos n.gen
## Q1  3@35.0   3  35.000     2
## Q2 4@112.0   4 112.000     2
## Q3  7@79.0   7  79.000     2
## Q4 10@67.7  10  67.674     2
population_Z006.cim.fit <- fitqtl(cross = population_Z006.mds, pheno.col = "PlantHeight", qtl = population_Z006.cim.qtl,
    formula = y ~ Q1 + Q2 + Q3, get.ests = TRUE)
summary(population_Z006.cim.fit)
## 
##      fitqtl summary
## 
## Method: multiple imputation 
## Model:  normal phenotype
## Number of observations : 185 
## 
## Full model result
## ----------------------------------  
## Model formula: y ~ Q1 + Q2 + Q3 
## 
##        df       SS        MS      LOD     %var Pvalue(Chi2)    Pvalue(F)
## Model   3 11885.79 3961.9312 12.54937 26.83032 1.741163e-12 2.986722e-12
## Error 181 32414.06  179.0832                                            
## Total 184 44299.86                                                      
## 
## 
## Drop one QTL at a time ANOVA table: 
## ----------------------------------  
##         df Type III SS   LOD   %var F value Pvalue(Chi2) Pvalue(F)    
## 3@35.0   1        2691 3.204  6.075   15.03            0  0.000148 ***
## 4@112.0  1        3472 4.088  7.837   19.39            0  1.82e-05 ***
## 7@79.0   1        5165 5.939 11.658   28.84            0  2.39e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Estimated effects:
## -----------------
##               est      SE       t
## Intercept 147.457   0.993 148.493
## 3@35.0     -4.034   1.051  -3.839
## 4@112.0    -4.405   1.035  -4.257
## 7@79.0      5.393   1.007   5.358
save.image("population_Z006_mds.RData")

Teremos maneiras melhores de verificar se há outros QTL nos cromossomos 1 e 3 sob a abordagem do modelo de Mapeamento de Intervalo Múltiplo.

Mapeamento de intervalo múltiplo

O Mapeamento de intervalo Multiplo usa vários intervalos de marcadores simultaneamente para ajustar vários QTL putativos diretamente no modelo para mapeamento de QTL.

Aqui, estou usando a linha de raça recombinante (RIL) população Z006, de modo que esses métodos podem não levar aos mesmos resultados em sua população.

library(qtl)
load("population_Z006_mds.RData")
summaryMap(population_Z006.mds)
##         n.mar length ave.spacing max.spacing
## 1         175  236.5         1.4        16.4
## 2         139  160.5         1.2        18.2
## 3         130  166.2         1.3        10.0
## 4         127  167.3         1.3        14.6
## 5         111  147.3         1.3        14.4
## 6         106  135.7         1.3        16.9
## 7          85  118.0         1.4        11.9
## 8          78  117.0         1.5        16.5
## 9          78  135.5         1.8        16.3
## 10         77  115.4         1.5        33.7
## overall  1106 1499.4         1.4        33.7
plotMap(population_Z006.mds)

plotRF(population_Z006.mds, col.scheme = "redblue")

Adicionando os principais efeitos manualmente.

R/qtl tem várias funções para lidar com modelos de QTL múltiplos. Vamos focar em alguns deles que são mais interessantes para este conjunto de dados específico.

makeqtl e fitqtl

Usei os resultados da execução qtl anterior, ou seja, mapeamento de intervalo composto.

Primeiro, podemos dar uma olhada no objeto (anteriormente nomeado ) do mapeamento de intervalo composto com execução: population_Z006.cim10

summary(population_Z006.cim10, perms = population_Z006.perm.cim, alpha = 0.05)
##             chr   pos  lod
## c4.loc113     4 113.0 11.4
## PZA02878.13   7  79.8 14.4

Notei que quatro picos tinham pontuação lod maior que nosso limiar cim de 7,29. No entanto, apenas um pico foi listado acima (o com a maior pontuação lod). Uma maneira de encontrar as outras posições é olhando para a pontuação máxima de LOD para posições no final do cromossomo 1 (posição > 150 cM):

max(population_Z006.cim10[population_Z006.cim10$chr == 1 & population_Z006.cim10$pos > 150, ])
##           chr pos  lod
## c1.loc193   1 193 7.16
chr <- c(1, 1, 4, 7, 10)
pos <- c(24, 192, 113, 77, 68.0)

Para construir um modelo de QTL múltiplo, precisamos usar a função, então: calc.genoprob() makeqtl()

population_Z006.mds <- calc.genoprob(cross = population_Z006.mds, step = 1)
population_Z006.qtl <- makeqtl(cross = population_Z006.mds, chr = chr, pos = pos)
population_Z006.qtl
##   QTL object containing imputed genotypes, with 16 imputations. 
## 
##       name chr pos n.gen
## Q1  1@24.0   1  24     2
## Q2 1@192.0   1 192     2
## Q3 4@113.0   4 113     2
## Q4  7@77.0   7  77     2
## Q5 10@68.0  10  68     2

A partir das avaliações consideraremos os seguintes QTLs e suas interações.

plot(population_Z006.qtl)

Então, encaixei esse modelo usando a função: fitqtl()

population_Z006.fit <- fitqtl(cross = population_Z006.mds, pheno.col = "PlantHeight", qtl = population_Z006.qtl,
    formula = y ~ Q1 + Q2 + Q3 + Q4 + Q5, get.ests = TRUE)
summary(population_Z006.fit)
## 
##      fitqtl summary
## 
## Method: multiple imputation 
## Model:  normal phenotype
## Number of observations : 185 
## 
## Full model result
## ----------------------------------  
## Model formula: y ~ Q1 + Q2 + Q3 + Q4 + Q5 
## 
##        df       SS        MS      LOD     %var Pvalue(Chi2) Pvalue(F)
## Model   5 16781.71 3356.3413 19.12744 37.88208            0         0
## Error 179 27518.15  153.7327                                         
## Total 184 44299.86                                                   
## 
## 
## Drop one QTL at a time ANOVA table: 
## ----------------------------------  
##         df Type III SS   LOD   %var F value Pvalue(Chi2) Pvalue(F)    
## 1@24.0   1      1840.7 2.601  4.155  11.974        0.001  0.000675 ***
## 1@192.0  1       599.7 0.866  1.354   3.901        0.046  0.049803 *  
## 4@113.0  1      3194.8 4.412  7.212  20.781        0.000  9.51e-06 ***
## 7@77.0   1      5833.1 7.723 13.167  37.943        0.000  4.68e-09 ***
## 10@68.0  1      5312.5 7.091 11.992  34.557        0.000  1.98e-08 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Estimated effects:
## -----------------
##                est       SE       t
## Intercept 147.7239   0.9318 158.538
## 1@24.0     -3.3473   0.9615  -3.481
## 1@192.0     1.7958   0.9238   1.944
## 4@113.0    -4.3115   0.9460  -4.557
## 7@77.0      5.6825   0.9285   6.120
## 10@68.0     5.4466   0.9357   5.821

Podemos observar que o QTL no cromossomo 1 e algumas das interações sugeridas não foram significativas ao nível de 5%, portanto podemos desconsiderá-los do modelo.

refineqtl e plotLodProfile

Toda vez que é encaixado um novo modelo de QTL múltiplo, recomenda-se que refinemos as posições de pico QTL porque pode haver alterações ao testar cada QTL condicional ao outro QTL no modelo. Executamos esta tarefa usando a função e plotamos os resultados usando: refineqtl() plotLodProfile()

population_Z006.ref <- refineqtl(cross = population_Z006.mds, pheno.col = "PlantHeight", qtl = population_Z006.qtl,
    formula = y ~ Q1 + Q2 + Q3 + Q4 + Q5)
## pos: 24 192 113 77 68 
## Iteration 1 
##  Q1 pos: 24 -> 155
##     LOD increase:  0.914 
##  Q4 pos: 77 -> 78.43663
##     LOD increase:  0.431 
##  Q5 pos: 68 -> 67.67358
##     LOD increase:  0.179 
##  Q2 pos: 192 -> 191.65
##     LOD increase:  0.035 
##  Q3 pos: 113 -> 114
##     LOD increase:  0.127 
## all pos: 24 192 113 77 68 -> 155 191.65 114 78.43663 67.67358 
## LOD increase at this iteration:  1.687 
## Iteration 2 
##  Q3 pos: 114 -> 114
##     LOD increase:  0 
##  Q4 pos: 78.43663 -> 78.43663
##     LOD increase:  0 
##  Q1 pos: 155 -> 157
##     LOD increase:  0.378 
##  Q5 pos: 67.67358 -> 67.67358
##     LOD increase:  0 
##  Q2 pos: 191.65 -> 191.65
##     LOD increase:  0 
## all pos: 155 191.65 114 78.43663 67.67358 -> 157 191.65 114 78.43663 67.67358 
## LOD increase at this iteration:  0.378 
## Iteration 3 
##  Q2 pos: 191.65 -> 191.65
##     LOD increase:  0 
##  Q1 pos: 157 -> 157
##     LOD increase:  0 
##  Q5 pos: 67.67358 -> 67.67358
##     LOD increase:  0 
##  Q3 pos: 114 -> 115.0743
##     LOD increase:  0.242 
##  Q4 pos: 78.43663 -> 78
##     LOD increase:  0.307 
## all pos: 157 191.65 114 78.43663 67.67358 -> 157 191.65 115.0743 78 67.67358 
## LOD increase at this iteration:  0.55 
## Iteration 4 
##  Q4 pos: 78 -> 78
##     LOD increase:  0 
##  Q1 pos: 157 -> 157
##     LOD increase:  0 
##  Q5 pos: 67.67358 -> 67.67358
##     LOD increase:  0 
##  Q2 pos: 191.65 -> 191.65
##     LOD increase:  0 
##  Q3 pos: 115.0743 -> 115.0743
##     LOD increase:  0 
## all pos: 157 191.65 115.0743 78 67.67358 -> 157 191.65 115.0743 78 67.67358 
## LOD increase at this iteration:  0 
## overall pos: 24 192 113 77 68 -> 157 191.65 115.0743 78 67.67358 
## LOD increase overall:  2.615
plotLodProfile(population_Z006.ref)

Em seguida, foi feito outra rodada de testes atualizados para as novas posições: fitqtl()

population_Z006.fit2 <- fitqtl(cross = population_Z006.mds, pheno.col = "PlantHeight", qtl = population_Z006.ref,
    formula = y ~ Q1 + Q2 + Q3 + Q4 + Q5, get.ests = TRUE)
summary(population_Z006.fit2)
## 
##      fitqtl summary
## 
## Method: multiple imputation 
## Model:  normal phenotype
## Number of observations : 185 
## 
## Full model result
## ----------------------------------  
## Model formula: y ~ Q1 + Q2 + Q3 + Q4 + Q5 
## 
##        df       SS       MS      LOD     %var Pvalue(Chi2) Pvalue(F)
## Model   5 18515.63 3703.125 21.74195 41.79613            0         0
## Error 179 25784.23  144.046                                         
## Total 184 44299.86                                                  
## 
## 
## Drop one QTL at a time ANOVA table: 
## ----------------------------------  
##         df Type III SS   LOD   %var F value Pvalue(Chi2) Pvalue(F)    
## 1@157.0  1        3045 4.485  6.874  21.141        0.000  8.04e-06 ***
## 1@191.7  1        1417 2.149  3.198   9.834        0.002     0.002 ** 
## 4@115.1  1        3055 4.498  6.896  21.208        0.000  7.79e-06 ***
## 7@78.0   1        6987 9.633 15.773  48.508        0.000  6.04e-11 ***
## 10@67.7  1        6297 8.778 14.215  43.718        0.000  4.22e-10 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Estimated effects:
## -----------------
##                est       SE       t
## Intercept 147.6114   0.8942 165.078
## 1@157.0    -4.2558   0.9833  -4.328
## 1@191.7     2.9112   0.9456   3.079
## 4@115.1    -4.1713   0.9227  -4.521
## 7@78.0      6.2472   0.9320   6.703
## 10@67.7     5.7798   0.9398   6.150

scantwo

Para adicionar efeitos principais ou epistáticos, precisamos descobrir qual é a “regra de parada”. Em R/qtl, tal regra é dada por pontuação de LOD penalizada para um modelo com mais de um QTL (função ) fornecido por permutações (). Esta função leva muito tempo para ser executada: scantwon.perm = 1000

set.seed(8617)
permo.2dim <- scantwo(population_Z006.mds, pheno.col = "PlantHeight", method = "hk", n.perm = 1000)
## Doing permutation in batch mode ...
save(permo.2dim, file = "permo_2dim.RData")

É sempre bom salvar saídas de permutação devido ao tempo que essas funções levam para serem executadas, para que você possa usar os resultados para a mesma característica mais tarde.

Agora, podemos ver um resumo do objeto e calcular as penalidades que serão usadas para pesquisa automática de modelos de vários QTL.

load("permo_2dim.RData")
summary(permo.2dim, alpha = 0.05)
##  (1000 permutations)
##    full  fv1  int  add  av1  one
## 5% 6.49 4.83 4.15 5.19 2.88 3.04
penalties <- calc.penalties(permo.2dim)
penalties
##     main    heavy    light 
## 3.037694 4.154428 1.797158

Limiares derivados de permutações (ou seja, para um escaneamento bidimensional do genoma de dois QTL) são usados para calcular penalidades sobre os principais efeitos e interações. Olhe para saber mais sobre as penalidades. Você também pode encontrar uma explicação detalhada sobre este critério por Manichaikul et al. (2009). scantwo help(calc.penalties)

addqtl

E o QTL on cromossomo 7? Se quisermos verificar se há mais QTL a ser adicionado ao modelo, podemos usar a função :addqtl()

population_Z006.add <- addqtl(population_Z006.mds, pheno.col = "PlantHeight", qtl = population_Z006.ref, formula = y ~
    Q1 + Q2 + Q3 + Q4 + Q5)
max(population_Z006.add)
##            chr  pos  lod
## PZA03070.9   3 54.1 3.21

O LOD > 3 parece grande o suficiente para incluir essa posição, já que o limite para adicionar um efeito principal av1= 3,06 (LOD “leve” para efeitos principais). Então, vamos adicioná-lo ao modelo.

chr <- c(1, 1, 4, 7, 10)
pos <- c(24, 192, 113, 77, 68.0)
population_Z006.qtl2 <- makeqtl(cross = population_Z006.mds, chr = chr, pos = pos)
population_Z006.qtl2
##   QTL object containing imputed genotypes, with 16 imputations. 
## 
##       name chr pos n.gen
## Q1  1@24.0   1  24     2
## Q2 1@192.0   1 192     2
## Q3 4@113.0   4 113     2
## Q4  7@77.0   7  77     2
## Q5 10@68.0  10  68     2
plot(population_Z006.qtl2)

Lembre-se que quando um QTL é adicionado, pode-se precisar refinar posições novamente usando a função: refineqtl()

population_Z006.ref2 <- refineqtl(cross = population_Z006.mds, pheno.col = "PlantHeight", qtl = population_Z006.qtl2,
    formula = y ~ Q1 + Q2 + Q3 + Q4 + Q5)
## pos: 24 192 113 77 68 
## Iteration 1 
##  Q5 pos: 68 -> 67.67358
##     LOD increase:  0.181 
##  Q3 pos: 113 -> 114
##     LOD increase:  0.276 
##  Q4 pos: 77 -> 70
##     LOD increase:  0.862 
##  Q2 pos: 192 -> 132
##     LOD increase:  0.934 
##  Q1 pos: 24 -> 24
##     LOD increase:  0 
## all pos: 24 192 113 77 68 -> 24 132 114 70 67.67358 
## LOD increase at this iteration:  2.254 
## Iteration 2 
##  Q1 pos: 24 -> 24
##     LOD increase:  0 
##  Q4 pos: 70 -> 71
##     LOD increase:  0.047 
##  Q3 pos: 114 -> 114
##     LOD increase:  0 
##  Q5 pos: 67.67358 -> 67
##     LOD increase:  0.114 
##  Q2 pos: 132 -> 132
##     LOD increase:  0 
## all pos: 24 132 114 70 67.67358 -> 24 132 114 71 67 
## LOD increase at this iteration:  0.161 
## Iteration 3 
##  Q2 pos: 132 -> 132
##     LOD increase:  0 
##  Q4 pos: 71 -> 71
##     LOD increase:  0 
##  Q1 pos: 24 -> 11.56667
##     LOD increase:  0.505 
##  Q5 pos: 67 -> 66
##     LOD increase:  0.132 
##  Q3 pos: 114 -> 112
##     LOD increase:  1.002 
## all pos: 24 132 114 71 67 -> 11.56667 132 112 71 66 
## LOD increase at this iteration:  1.639 
## Iteration 4 
##  Q3 pos: 112 -> 112
##     LOD increase:  0 
##  Q5 pos: 66 -> 66
##     LOD increase:  0 
##  Q2 pos: 132 -> 134
##     LOD increase:  0.01 
##  Q1 pos: 11.56667 -> 11.56667
##     LOD increase:  0 
##  Q4 pos: 71 -> 71
##     LOD increase:  0 
## all pos: 11.56667 132 112 71 66 -> 11.56667 134 112 71 66 
## LOD increase at this iteration:  0.01 
## Iteration 5 
##  Q4 pos: 71 -> 71
##     LOD increase:  0 
##  Q3 pos: 112 -> 112
##     LOD increase:  0 
##  Q1 pos: 11.56667 -> 11.56667
##     LOD increase:  0 
##  Q5 pos: 66 -> 66
##     LOD increase:  0 
##  Q2 pos: 134 -> 134
##     LOD increase:  0 
## all pos: 11.56667 134 112 71 66 -> 11.56667 134 112 71 66 
## LOD increase at this iteration:  0 
## overall pos: 24 192 113 77 68 -> 11.56667 134 112 71 66 
## LOD increase overall:  4.064
plotLodProfile(population_Z006.ref2)

population_Z006.add2 <- addqtl(population_Z006.mds, pheno.col = "PlantHeight", qtl = population_Z006.ref2, formula = y ~
    Q1 + Q2 + Q3 + Q4 + Q5)
max(population_Z006.add2)
##          chr pos  lod
## c3.loc38   3  38 4.25

Porque o LOD é inferior ao nosso limiar de 3.06, então paramos aqui e podemos tentar adicionar efeitos epistáticos em seguida.

Adicionando efeitos principais automaticamente

stepwiseqtl

As penalidades para as pontuações de LOD penalizadas agora podem ser usadas por: stepwiseqtl

population_Z006.step <- stepwiseqtl(population_Z006.mds, pheno.col = "PlantHeight", max.qtl = 6, method = "hk",
    penalties = penalties)
##  -Initial scan
## initial lod:  5.289028 
## ** new best ** (pLOD increased by 2.2513)
##     no.qtl =  1   pLOD = 2.251334   formula: y ~ Q1 
##  -Step 1 
##  ---Scanning for additive qtl
##         plod = 5.593692 
##  ---Scanning for QTL interacting with Q1
##         plod = 4.613319 
##  ---Refining positions
##     no.qtl =  2   pLOD = 5.593692   formula: y ~ Q1 + Q2 
## ** new best ** (pLOD increased by 3.3424)
##  -Step 2 
##  ---Scanning for additive qtl
##         plod = 7.908522 
##  ---Scanning for QTL interacting with Q1
##         plod = 6.119383 
##  ---Scanning for QTL interacting with Q2
##         plod = 6.113402 
##  ---Look for additional interactions
##         plod = 4.133686 
##  ---Refining positions
##     no.qtl =  3   pLOD = 7.908522   formula: y ~ Q1 + Q2 + Q3 
## ** new best ** (pLOD increased by 2.3148)
##  -Step 3 
##  ---Scanning for additive qtl
##         plod = 8.835737 
##  ---Scanning for QTL interacting with Q1
##         plod = 7.685776 
##  ---Scanning for QTL interacting with Q2
##         plod = 7.042183 
##  ---Scanning for QTL interacting with Q3
##         plod = 7.051611 
##  ---Look for additional interactions
##         plod = 6.547946 
##  ---Refining positions
##  ---  Moved a bit
##     no.qtl =  4   pLOD = 9.040116   formula: y ~ Q1 + Q2 + Q3 + Q4 
## ** new best ** (pLOD increased by 1.1316)
##  -Step 4 
##  ---Scanning for additive qtl
##         plod = 9.573872 
##  ---Scanning for QTL interacting with Q1
##         plod = 7.815094 
##  ---Scanning for QTL interacting with Q2
##         plod = 8.069141 
##  ---Scanning for QTL interacting with Q3
##         plod = 7.878503 
##  ---Scanning for QTL interacting with Q4
##         plod = 7.78588 
##  ---Look for additional interactions
##         plod = 7.828348 
##  ---Refining positions
##  ---  Moved a bit
##     no.qtl =  5   pLOD = 9.642027   formula: y ~ Q1 + Q2 + Q3 + Q4 + Q5 
## ** new best ** (pLOD increased by 0.6019)
##  -Step 5 
##  ---Scanning for additive qtl
##         plod = 11.02829 
##  ---Scanning for QTL interacting with Q1
##         plod = 9.730044 
##  ---Scanning for QTL interacting with Q2
##         plod = 9.274608 
##  ---Scanning for QTL interacting with Q3
##         plod = 9.682936 
##  ---Scanning for QTL interacting with Q4
##         plod = 9.455829 
##  ---Scanning for QTL interacting with Q5
##         plod = 9.440745 
##  ---Look for additional interactions
##         plod = 8.572873 
##  ---Refining positions
##     no.qtl =  6   pLOD = 11.02829   formula: y ~ Q1 + Q2 + Q3 + Q4 + Q5 + Q6 
## ** new best ** (pLOD increased by 1.3863)
##  -Starting backward deletion
##  ---Dropping Q6 
##     no.qtl =  5   pLOD = 9.642027   formula: y ~ Q1 + Q2 + Q3 + Q4 + Q5 
##  ---Refining positions
##  ---Dropping Q5 
##     no.qtl =  4   pLOD = 9.038169   formula: y ~ Q1 + Q2 + Q3 + Q4 
##  ---Refining positions
##  ---  Moved a bit
##  ---Dropping Q4 
##     no.qtl =  3   pLOD = 7.675221   formula: y ~ Q1 + Q2 + Q3 
##  ---Refining positions
##  ---  Moved a bit
##  ---Dropping Q3 
##     no.qtl =  2   pLOD = 4.464548   formula: y ~ Q1 + Q2 
##  ---Refining positions
##  ---  Moved a bit
##  ---Dropping Q2 
##     no.qtl =  1   pLOD = 2.251334   formula: y ~ Q1 
##  ---Refining positions
##  ---One last pass through refineqtl
population_Z006.step
##   QTL object containing genotype probabilities. 
## 
##       name chr     pos n.gen
## Q1  1@24.0   1  24.000     2
## Q2  3@55.0   3  55.011     2
## Q3 4@113.0   4 113.000     2
## Q4  7@82.0   7  82.000     2
## Q5  9@85.9   9  85.864     2
## Q6 10@64.3  10  64.274     2
## 
##   Formula: y ~ Q1 + Q2 + Q3 + Q4 + Q5 + Q6 
## 
##   pLOD:  11.028

Adicionando epistasia

Na corrida acima, já testamos interações entre QTL no modelo (e não foram encontradas interações). stepwiseqtl

addint

No caso de nossa pesquisa manual, a QTL interativa ainda não foi testada. Uma maneira de procurar explicitamente a epistasis é usando a função, por isso usamos nosso objeto: addint() population_Z006.ref

addint(population_Z006.mds, pheno.col = "PlantHeight", qtl = population_Z006.ref2, formula = y ~ Q1 +
    Q2 + Q3 + Q4 +Q5)
## Method: multiple imputation 
## Model:  normal phenotype
## Model formula: y ~ Q1 + Q2 + Q3 + Q4 + Q5 
## 
## Add one pairwise interaction at a time table:
## --------------------------------------------
##                 df Type III SS      LOD     %var  F value Pvalue(Chi2)
## 1@11.6:1@134.0   1       30.37  0.04908  0.06855  0.21759        0.634
## 1@11.6:4@112.0   1       11.63  0.01879  0.02626  0.08329        0.769
## 1@11.6:7@71.0    1      207.42  0.33643  0.46821  1.49698        0.213
## 1@11.6:10@66.0   1      -14.98 -0.02418 -0.03381 -0.10712        1.000
## 1@134.0:4@112.0  1      -23.76 -0.03837 -0.05364 -0.16992        1.000
## 1@134.0:7@71.0   1      438.96  0.71537  0.99089  3.19813        0.070
## 1@134.0:10@66.0  1      386.89  0.62984  0.87335  2.81277        0.089
## 4@112.0:7@71.0   1      401.16  0.65326  0.90556  2.91821        0.083
## 4@112.0:10@66.0  1       66.95  0.10828  0.15112  0.48045        0.480
## 7@71.0:10@66.0   1      494.16  0.80623  1.11549  3.60844        0.054
##                 Pvalue(F)  
## 1@11.6:1@134.0     0.6414  
## 1@11.6:4@112.0     0.7732  
## 1@11.6:7@71.0      0.2227  
## 1@11.6:10@66.0     1.0000  
## 1@134.0:4@112.0    1.0000  
## 1@134.0:7@71.0     0.0754 .
## 1@134.0:10@66.0    0.0952 .
## 4@112.0:7@71.0     0.0893 .
## 4@112.0:10@66.0    0.4891  
## 7@71.0:10@66.0     0.0591 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Como todos os LODs estão abaixo de 4,21 ( limiar de LOD de nossas permutações acima), determinamos que não há evidência de epistasis entre os QTL.

scantwo

Olhar para interações epistáticas entre QTL com efeitos principais é muito limitador. Então, podemos usar a função para procurar evidências de interações por todo o genoma. Além disso, devemos ser capazes de separar pares de QTL ligados com tal função.scantwo()

Por se trata de uma pesquisa muito computacionalmente intensiva, usamos para acelerar um pouco o processo: step = 2

population_Z006.mds <- calc.genoprob(population_Z006.mds, step = 2)
population_Z006.two <- scantwo(population_Z006.mds, pheno.col = "PlantHeight", method = "hk")
##  --Running scanone
##  --Running scantwo
##  (1,1)
##  (1,2)
##  (1,3)
##  (1,4)
##  (1,5)
##  (1,6)
##  (1,7)
##  (1,8)
##  (1,9)
##  (1,10)
##  (2,2)
##  (2,3)
##  (2,4)
##  (2,5)
##  (2,6)
##  (2,7)
##  (2,8)
##  (2,9)
##  (2,10)
##  (3,3)
##  (3,4)
##  (3,5)
##  (3,6)
##  (3,7)
##  (3,8)
##  (3,9)
##  (3,10)
##  (4,4)
##  (4,5)
##  (4,6)
##  (4,7)
##  (4,8)
##  (4,9)
##  (4,10)
##  (5,5)
##  (5,6)
##  (5,7)
##  (5,8)
##  (5,9)
##  (5,10)
##  (6,6)
##  (6,7)
##  (6,8)
##  (6,9)
##  (6,10)
##  (7,7)
##  (7,8)
##  (7,9)
##  (7,10)
##  (8,8)
##  (8,9)
##  (8,10)
##  (9,9)
##  (9,10)
##  (10,10)
save(population_Z006.two, file = "population_Z006_two.RData")
load("population_Z006_two.RData")
plot(population_Z006.two, col.scheme = "redblue")

summary(population_Z006.two)
##         pos1f pos2f lod.full lod.fv1 lod.int     pos1a pos2a lod.add lod.av1
## c1 :c1    158   190     4.50   1.760 0.47565        26   156   4.024   1.284
## c1 :c2    156    70     4.26   1.518 0.74081        24   122   3.517   0.778
## c1 :c3     24    34     6.49   2.611 0.07160        24    36   6.414   2.539
## c1 :c4     26   112     6.82   3.526 0.87538        24   112   5.943   2.651
## c1 :c5     22    10     5.50   2.764 0.36089        24    34   5.142   2.403
## c1 :c6     26   118     3.54   0.799 0.18695        24    62   3.352   0.612
## c1 :c7     24    70     8.52   3.381 0.00141        24    70   8.523   3.380
## c1 :c8     22   102     5.88   3.141 2.46038        24   112   3.420   0.680
## c1 :c9      2    86     6.30   3.559 0.36185        26    86   5.937   3.197
## c1 :c10    22    68     9.37   4.117 0.81979        22    68   8.545   3.297
## c2 :c2     56    60     2.28   1.789 0.00965        56    60   2.268   1.779
## c2 :c3     32    34     5.69   1.813 1.27530       116    36   4.413   0.538
## c2 :c4     82   112     4.44   1.144 0.76953       116   112   3.667   0.375
## c2 :c5     68     8     3.41   1.453 1.00047       114    34   2.409   0.452
## c2 :c6     54   102     1.24   0.753 0.25391       116    62   0.989   0.499
## c2 :c7     64    86     7.51   2.367 1.27071       116    82   6.239   1.096
## c2 :c8    112   114     2.22   1.454 0.96338       116    38   1.259   0.491
## c2 :c9     50    80     3.32   0.875 0.53653        54    86   2.780   0.338
## c2 :c10    86    68     6.53   1.286 0.82034       108    68   5.715   0.466
## c3 :c3     36   126     5.14   1.270 0.44470        36   134   4.700   0.825
## c3 :c4     34   112     7.39   3.516 0.01971        34   112   7.371   3.496
## c3 :c5     36     8     7.82   3.950 2.20550        36    20   5.619   1.744
## c3 :c6     36     0     5.36   1.483 0.93234        36    62   4.426   0.551
## c3 :c7     36    82     8.93   3.790 0.02097        36    82   8.912   3.769
## c3 :c8     36    38     5.22   1.344 0.33983        36   110   4.879   1.004
## c3 :c9     36    80     6.80   2.921 0.03871        36    80   6.758   2.882
## c3 :c10    36    68     9.02   3.774 0.00807        36    68   9.014   3.766
## c4 :c4      6   112     4.79   1.499 0.12532        60   112   4.666   1.374
## c4 :c5    112    26     5.72   2.427 0.49356       112    34   5.226   1.934
## c4 :c6    112    48     4.64   1.351 0.78855       112     8   3.855   0.563
## c4 :c7    116    82    10.06   4.915 0.08615       116    78   9.971   4.828
## c4 :c8    112    82     6.12   2.833 1.89842       112    80   4.227   0.934
## c4 :c9    112    86     6.65   3.361 0.07639       112    86   6.577   3.284
## c4 :c10   118    98     9.04   3.796 0.35144       112    62   8.693   3.445
## c5 :c5     34   120     3.56   1.602 0.01141        34   120   3.547   1.591
## c5 :c6      2    48     3.69   1.737 1.32917        34    62   2.364   0.408
## c5 :c7      6    82     8.72   3.574 1.36185        12    60   7.356   2.213
## c5 :c8     20   110     3.55   1.595 0.63507        34   110   2.916   0.960
## c5 :c9     10    86     5.49   3.047 0.78199        34    86   4.707   2.265
## c5 :c10    48    68     8.35   3.104 1.11136        34    68   7.241   1.993
## c6 :c6     62    64     3.10   2.633 1.90892        48    50   1.193   0.724
## c6 :c7      0    60     6.48   1.340 0.73159        62    70   5.751   0.608
## c6 :c8      6    38     1.93   1.159 0.73411        62   110   1.193   0.425
## c6 :c9     40    84     3.00   0.559 0.01172        62    86   2.989   0.547
## c6 :c10    78    68     6.64   1.393 0.89420       112    68   5.748   0.499
## c7 :c7     60    86     7.60   2.457 0.04731        60    82   7.553   2.410
## c7 :c8     88   110     7.21   2.071 0.82849        82   110   6.386   1.243
## c7 :c9     72    78     8.62   3.475 1.17323        82    86   7.445   2.302
## c7 :c10    72    68    12.34   7.092 0.85658        78    68  11.484   6.235
## c8 :c8     38    60     2.96   2.192 1.36403        38   110   1.597   0.828
## c8 :c9    110    86     3.36   0.917 0.13600       110    86   3.223   0.781
## c8 :c10     4    68     6.11   0.866 0.35597        38    68   5.759   0.510
## c9 :c9      4    86     3.98   1.538 1.14256        62    86   2.837   0.396
## c9 :c10    86    68     7.35   2.101 0.04101        86    68   7.308   2.060
## c10:c10    68   108     7.07   1.825 0.09247        68   108   6.981   1.733

A partir do gráfico e resumo acima, não há interações entre os loci nos cromossomos.

chr <- c(1, 1, 4, 7, 10)
pos <- c(24, 192, 113, 77, 68.0)
population_Z006.qtl3 <- makeqtl(cross = population_Z006.mds, chr = chr, pos = pos)
population_Z006.qtl3
##   QTL object containing imputed genotypes, with 16 imputations. 
## 
##       name chr pos n.gen
## Q1  1@24.0   1  24     2
## Q2 1@192.0   1 192     2
## Q3 4@113.0   4 113     2
## Q4  7@77.0   7  77     2
## Q5 10@68.0  10  68     2
population_Z006.ref3 <- refineqtl(cross = population_Z006.mds, pheno.col = "PlantHeight", qtl = population_Z006.qtl3,
    formula = y ~ Q1 + Q2 + Q3 + Q4 +Q5)
## pos: 24 192 113 77 68 
## Iteration 1 
##  Q4 pos: 77 -> 70
##     LOD increase:  1.194 
##  Q5 pos: 68 -> 67.67358
##     LOD increase:  0.194 
##  Q2 pos: 192 -> 132
##     LOD increase:  1.015 
##  Q3 pos: 113 -> 113
##     LOD increase:  0 
##  Q1 pos: 24 -> 11.56667
##     LOD increase:  0.13 
## all pos: 24 192 113 77 68 -> 11.56667 132 113 70 67.67358 
## LOD increase at this iteration:  2.533 
## Iteration 2 
##  Q1 pos: 11.56667 -> 11.56667
##     LOD increase:  0 
##  Q2 pos: 132 -> 132
##     LOD increase:  0 
##  Q4 pos: 70 -> 69
##     LOD increase:  0.232 
##  Q3 pos: 113 -> 113
##     LOD increase:  0 
##  Q5 pos: 67.67358 -> 66
##     LOD increase:  0.731 
## all pos: 11.56667 132 113 70 67.67358 -> 11.56667 132 113 69 66 
## LOD increase at this iteration:  0.963 
## Iteration 3 
##  Q5 pos: 66 -> 66
##     LOD increase:  0 
##  Q2 pos: 132 -> 133
##     LOD increase:  0.199 
##  Q3 pos: 113 -> 113
##     LOD increase:  0 
##  Q1 pos: 11.56667 -> 11.56667
##     LOD increase:  0 
##  Q4 pos: 69 -> 71
##     LOD increase:  0.074 
## all pos: 11.56667 132 113 69 66 -> 11.56667 133 113 71 66 
## LOD increase at this iteration:  0.272 
## Iteration 4 
##  Q4 pos: 71 -> 71
##     LOD increase:  0 
##  Q1 pos: 11.56667 -> 11.56667
##     LOD increase:  0 
##  Q5 pos: 66 -> 66
##     LOD increase:  0 
##  Q3 pos: 113 -> 112
##     LOD increase:  0.254 
##  Q2 pos: 133 -> 134
##     LOD increase:  0.041 
## all pos: 11.56667 133 113 71 66 -> 11.56667 134 112 71 66 
## LOD increase at this iteration:  0.296 
## Iteration 5 
##  Q2 pos: 134 -> 134
##     LOD increase:  0 
##  Q4 pos: 71 -> 71
##     LOD increase:  0 
##  Q5 pos: 66 -> 66
##     LOD increase:  0 
##  Q1 pos: 11.56667 -> 11.56667
##     LOD increase:  0 
##  Q3 pos: 112 -> 112
##     LOD increase:  0 
## all pos: 11.56667 134 112 71 66 -> 11.56667 134 112 71 66 
## LOD increase at this iteration:  0 
## overall pos: 24 192 113 77 68 -> 11.56667 134 112 71 66 
## LOD increase overall:  4.064
summary(population_Z006.ref3)
##   QTL object containing imputed genotypes, with 16 imputations. 
## 
##       name chr     pos n.gen
## Q1  1@11.6   1  11.567     2
## Q2 1@134.0   1 134.000     2
## Q3 4@112.0   4 112.000     2
## Q4  7@71.0   7  71.000     2
## Q5 10@66.0  10  66.000     2
plotLodProfile(population_Z006.ref3)

population_Z006.fit3 <- fitqtl(cross = population_Z006.mds, pheno.col = "PlantHeight", qtl = population_Z006.ref3,
    formula = y ~ Q1 + Q2 + Q3 + Q4 +Q5, get.ests = TRUE)
summary(population_Z006.fit3)
## 
##      fitqtl summary
## 
## Method: multiple imputation 
## Model:  normal phenotype
## Number of observations : 185 
## 
## Full model result
## ----------------------------------  
## Model formula: y ~ Q1 + Q2 + Q3 + Q4 + Q5 
## 
##        df       SS        MS      LOD     %var Pvalue(Chi2) Pvalue(F)
## Model   5 19429.35 3885.8709 23.19139 43.85873            0         0
## Error 179 24870.50  138.9414                                         
## Total 184 44299.86                                                   
## 
## 
## Drop one QTL at a time ANOVA table: 
## ----------------------------------  
##         df Type III SS    LOD   %var F value Pvalue(Chi2) Pvalue(F)    
## 1@11.6   1        4276  6.373  9.652  30.773        0.000  1.03e-07 ***
## 1@134.0  1        1271  2.002  2.868   9.145        0.002   0.00286 ** 
## 4@112.0  1        4863  7.175 10.978  35.001        0.000  1.64e-08 ***
## 7@71.0   1        7785 10.940 17.573  56.028        0.000  3.12e-12 ***
## 10@66.0  1        7231 10.253 16.322  52.042        0.000  1.48e-11 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Estimated effects:
## -----------------
##                est       SE       t
## Intercept 147.6777   0.8948 165.048
## 1@11.6     -4.6729   0.9603  -4.866
## 1@134.0    -2.1688   0.9187  -2.361
## 4@112.0    -4.5862   0.8918  -5.142
## 7@71.0      6.2045   0.8921   6.955
## 10@66.0     5.7977   0.8949   6.478

  1. Federal Universidade Federal de Viçosa, ↩︎